"In 2019, the football transfer market reached unprecedented levels both in terms of the number of transfers and the amount spent on fees.."
James Kitching,Director of Football Regulator
Growing football industry can cause clubs spend higher than what they possibly win. UEFA brought out Financial Fair Play Regulations to audit the expenses of clubs and prevent them to collapse financially.UEFA explained those regulations as improving the overall financial health of European club football.To follow the rules in the regulation, valuation of a player becomes crucial either to sell or to buy.
Fifa published a report in 2019, showing the statistics about transfers in 2019. In the report, it is highlighted many times that football teams can give lots of money to be in the competition with others.
Above chart shows how fast the football transfer industry is growing.It reached ~7.5 billion USD in 2019.
Above table shows the numbers of transfers are increasing, average fees are increasing as well as years pass.
What makes a football player expensive? Can the fee be predicted? I decided to go on these questions and by using the world-wide known website "transfermarkt" I built a model on transfer fees. I started firstly scraping transfer data from the transfermarkt
I decided to scrape all the transfers with fee,player profile all the matches a football player played before transfer date, achievements and player transfer history using Beautifusoup. All detailed scraping codes can be reachable in my github repository
Below I will reason behind scraping specific link and the features i produced after those scraping codes.
Transfers are the main table in which there are all the transfer history of all leagues that transfermarkt has. I decided to scrape all the transfers from the link. This link shows the transfers in each date as shown below.

I looped from all the pages and dates from 2016 to 2020. Finally i eliminated loans & Free transfers to get paid transfers. Data pattern of transfer data is :
import pandas as pd
pd.set_option('max_info_columns', 500)
transfers = pd.read_pickle('C:/Users/YAVUZ/Transfer Fee Project/0.Web Scraping/0.Transfers/Paid_Transfers_2016_2020.pkl')
transfers.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 9499 entries, 0 to 294176 Data columns (total 14 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Transfer_Link 9499 non-null object 1 Transfer_id 9499 non-null object 2 Transfer_Date 9499 non-null object 3 Page_Number 9499 non-null object 4 id 9499 non-null object 5 Link 9499 non-null object 6 Player_Name 9499 non-null object 7 Player_Position 9499 non-null object 8 Age 9499 non-null object 9 Club_Left 9499 non-null object 10 League_Left 9499 non-null object 11 Club_Joined 9499 non-null object 12 League_Joined 9499 non-null object 13 Transfer_Fee 9499 non-null object dtypes: object(14) memory usage: 1.1+ MB
Transfer details are the source of the transfer history of a player. Also in each transfer, specific info is provided such as age at the time of transfer, market value at the time of transfer, remaining contract .The transfer detail website for a player can be reached from here.

I looped for all the players in paid transfer dataset.I also created some other features like Number of transfers in transfer history, Avg Market Value, Total Fee, Avg Fee etc. I also created flag features such as: Country_Change_Flag, League_Change_Flag,League_Tier_Up_Flag,League_Tier_Down_Flag
I also created another dataset which shows the team a player played in each date.
Data patterns of transfer detail and team played datasets are :
transfer_detail = pd.read_pickle('C:/Users/YAVUZ/Transfer Fee Project/0.Web Scraping/1.Transfer Details/Transfers_Hst_Detail2.pkl')
transfer_detail.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 9499 entries, 0 to 9498 Data columns (total 26 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Transfer_Link 9499 non-null object 1 Transfer_id 9499 non-null object 2 id 9499 non-null object 3 NOF_Transfers 9499 non-null object 4 Avg_Market_Value 7729 non-null object 5 Tot_Fee_Loan_Fee_Exc 9499 non-null object 6 Avg_Fee 4166 non-null object 7 Tot_Fee_Loan_Fee_Inc 9499 non-null object 8 Age_at_Time_of_transfer 9499 non-null object 9 Contract_Left_at_Time_of_transfer 7496 non-null object 10 Market_Value_at_Time_of_transfer 9499 non-null object 11 Team_Left_Tier 9447 non-null object 12 Team_Joined_Tier 9447 non-null object 13 Team_Left_League 9447 non-null object 14 Team_Joined_League 9447 non-null object 15 Fee 9499 non-null object 16 Transfer_Season 9499 non-null object 17 Transfer_Date 9499 non-null object 18 Team_Left 9499 non-null object 19 Team_Joined 9499 non-null object 20 Country_Left 9141 non-null object 21 Country_Joined 9371 non-null object 22 Country_Change_Flag 9499 non-null object 23 League_Change_Flag 9499 non-null object 24 League_Tier_Up_Flag 9499 non-null object 25 League_Tier_Down_Flag 9499 non-null object dtypes: object(26) memory usage: 1.9+ MB
team_played = pd.read_pickle('C:/Users/YAVUZ/Transfer Fee Project/0.Web Scraping/1.Transfer Details/Team_played_All.pkl')
team_played.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 84748 entries, 0 to 84747 Data columns (total 7 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Team 84748 non-null object 1 Start 84748 non-null datetime64[ns] 2 End 75250 non-null datetime64[ns] 3 Transfer_Link 84748 non-null object 4 Transfer_id 84748 non-null object 5 id 84748 non-null object 6 Transfer_Date 84748 non-null object dtypes: datetime64[ns](2), object(5) memory usage: 4.5+ MB
Player profile is providing the general information about the footballer such as citizenship, height,preferred foot, positions etc. You can reach a player's profile page from here. Below screenshot is showing available information about a player's profile.

The data pattern for profile is:
#loading player profile features from pkl
profile=pd.read_pickle('C:/Users/YAVUZ/Transfer Fee Project/0.Web Scraping/2.Player_Profiler/Player_profile.pkl')
profile.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 9499 entries, 0 to 9498 Data columns (total 13 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Transfer_Link 9499 non-null object 1 Transfer_id 9499 non-null object 2 id 9499 non-null object 3 Transfer_Date 9499 non-null object 4 Date_of_birth 9499 non-null object 5 Place_of_birth 9499 non-null object 6 Height 9499 non-null object 7 Citizenship 9499 non-null object 8 Position 9499 non-null object 9 Foot 9499 non-null object 10 Main_position 9499 non-null object 11 other_position1 9499 non-null object 12 other_position2 9499 non-null object dtypes: object(13) memory usage: 964.9+ KB
This was the hard part of the project. In transfermarkt, stats are given in each season, however a footballer can change his club during the season. Since I wanted to get the stats before the transfers, I could not use the stats of website, so i decided to create my own features by scraping all the matches a footballer played. The scraping code is available in github, however dataset is not available, since it's bigger than github's limit.The matches are available in the link

Later I created features like , how many times of a player played,avg minutes, goals, assists, benched times, substituted games,injuries,suspensions,match points, etc in last 5,10,20,30 games before transfer date.. To do this i firstly worked on "WinLoseDraw.ipynb" to get win/draw or lose information. Then I created features in "Stats_Features.ipynb" in feature engineering folder.
The data pattern of stats features datasets is:
stats = pd.read_pickle('C:/Users/YAVUZ/Transfer Fee Project/1.Feature Engineering/Stats_Features_All.pkl')
stats.info()
<class 'pandas.core.frame.DataFrame'> Index: 9381 entries, 3105419 to 1115385 Data columns (total 200 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Date_30 9381 non-null int64 1 starting_lineup_30 9381 non-null int32 2 matches_played_flag_30 9381 non-null int32 3 Goals_30 9381 non-null float64 4 Assists_30 9381 non-null float64 5 Own goals_30 9381 non-null float64 6 yellow_card_flag_30 9381 non-null int32 7 second_yellow_card_flag_30 9381 non-null int32 8 red_card_flag_30 9381 non-null int32 9 Minutes played_30 9381 non-null float64 10 substitution_off_flag_30 9381 non-null int32 11 match_played_rate_30 9381 non-null float64 12 rate_subs_off_30 9208 non-null float64 13 match_points_played_30 9282 non-null float64 14 match_points_notplayed_30 9098 non-null float64 15 total_goals_team_30 9287 non-null float64 16 total_goals_team_played_30 9002 non-null float64 17 total_goals_team_conceded_30 9287 non-null float64 18 total_goals_team_conceded_played_30 9002 non-null float64 19 clean_sheet_flg_played_30 9282 non-null float64 20 clean_sheet_flg_notplayed_30 9098 non-null float64 21 lineup_rate_30 9282 non-null float64 22 minutes_per_game_30 9282 non-null float64 23 goals_per_Game_30 9282 non-null float64 24 assists_per_game_30 9282 non-null float64 25 own_goals_per_game_30 9282 non-null float64 26 yellow_cards_per_game_30 9282 non-null float64 27 second_yellow_card_per_game_30 9282 non-null float64 28 red_card_per_game_30 9282 non-null float64 29 team_goals_per_game_30 9002 non-null float64 30 points_per_game_30 9282 non-null float64 31 team_conceeded_goals_per_game_30 9002 non-null float64 32 clean_sheet_per_game_30 9282 non-null float64 33 team_goals_per_game_not_played_30 8724 non-null float64 34 clean_sheet_per_game_not_played_30 9098 non-null float64 35 team_conceeded_goals_per_game_not_played_30 8724 non-null float64 36 points_per_game_not_played_30 9098 non-null float64 37 minutes_played_coverage_played_30 9282 non-null float64 38 minutes_played_coverage_all_30 9381 non-null float64 39 points_played_coverage_30 9282 non-null float64 40 points_not_played_coverage_30 9098 non-null float64 41 Injury_30 2108 non-null float64 42 Suspension_30 2218 non-null float64 43 not in squad_30 7288 non-null float64 44 on the bench _30 6088 non-null float64 45 injury_rate_30 2108 non-null float64 46 suspension_rate_30 2218 non-null float64 47 on_the_bench_rate_30 6088 non-null float64 48 not_in_squad_rate_30 7288 non-null float64 49 last_played_game_recency_30 9381 non-null timedelta64[ns] 50 Date_20 9381 non-null int64 51 starting_lineup_20 9381 non-null int32 52 matches_played_flag_20 9381 non-null int32 53 Goals_20 9381 non-null float64 54 Assists_20 9381 non-null float64 55 Own goals_20 9381 non-null float64 56 yellow_card_flag_20 9381 non-null int32 57 second_yellow_card_flag_20 9381 non-null int32 58 red_card_flag_20 9381 non-null int32 59 Minutes played_20 9381 non-null float64 60 substitution_off_flag_20 9381 non-null int32 61 match_played_rate_20 9381 non-null float64 62 rate_subs_off_20 9046 non-null float64 63 match_points_played_20 9180 non-null float64 64 match_points_notplayed_20 8747 non-null float64 65 total_goals_team_20 9268 non-null float64 66 total_goals_team_played_20 8759 non-null float64 67 total_goals_team_conceded_20 9268 non-null float64 68 total_goals_team_conceded_played_20 8759 non-null float64 69 clean_sheet_flg_played_20 9180 non-null float64 70 clean_sheet_flg_notplayed_20 8747 non-null float64 71 lineup_rate_20 9180 non-null float64 72 minutes_per_game_20 9180 non-null float64 73 goals_per_Game_20 9180 non-null float64 74 assists_per_game_20 9180 non-null float64 75 own_goals_per_game_20 9180 non-null float64 76 yellow_cards_per_game_20 9180 non-null float64 77 second_yellow_card_per_game_20 9180 non-null float64 78 red_card_per_game_20 9180 non-null float64 79 team_goals_per_game_20 8759 non-null float64 80 points_per_game_20 9180 non-null float64 81 team_conceeded_goals_per_game_20 8759 non-null float64 82 clean_sheet_per_game_20 9180 non-null float64 83 team_goals_per_game_not_played_20 8133 non-null float64 84 clean_sheet_per_game_not_played_20 8747 non-null float64 85 team_conceeded_goals_per_game_not_played_20 8133 non-null float64 86 points_per_game_not_played_20 8747 non-null float64 87 minutes_played_coverage_played_20 9180 non-null float64 88 minutes_played_coverage_all_20 9381 non-null float64 89 points_played_coverage_20 9180 non-null float64 90 points_not_played_coverage_20 8747 non-null float64 91 Injury_20 1652 non-null float64 92 Suspension_20 1745 non-null float64 93 not in squad_20 6358 non-null float64 94 on the bench _20 4990 non-null float64 95 injury_rate_20 1652 non-null float64 96 suspension_rate_20 1745 non-null float64 97 on_the_bench_rate_20 4990 non-null float64 98 not_in_squad_rate_20 6358 non-null float64 99 last_played_game_recency_20 9381 non-null timedelta64[ns] 100 Date_10 9381 non-null int64 101 starting_lineup_10 9381 non-null int32 102 matches_played_flag_10 9381 non-null int32 103 Goals_10 9381 non-null float64 104 Assists_10 9381 non-null float64 105 Own goals_10 9381 non-null float64 106 yellow_card_flag_10 9381 non-null int32 107 second_yellow_card_flag_10 9381 non-null int32 108 red_card_flag_10 9381 non-null int32 109 Minutes played_10 9381 non-null float64 110 substitution_off_flag_10 9381 non-null int32 111 match_played_rate_10 9381 non-null float64 112 rate_subs_off_10 8523 non-null float64 113 match_points_played_10 8868 non-null float64 114 match_points_notplayed_10 7658 non-null float64 115 total_goals_team_10 9261 non-null float64 116 total_goals_team_played_10 8149 non-null float64 117 total_goals_team_conceded_10 9261 non-null float64 118 total_goals_team_conceded_played_10 8149 non-null float64 119 clean_sheet_flg_played_10 8868 non-null float64 120 clean_sheet_flg_notplayed_10 7658 non-null float64 121 lineup_rate_10 8868 non-null float64 122 minutes_per_game_10 8868 non-null float64 123 goals_per_Game_10 8868 non-null float64 124 assists_per_game_10 8868 non-null float64 125 own_goals_per_game_10 8868 non-null float64 126 yellow_cards_per_game_10 8868 non-null float64 127 second_yellow_card_per_game_10 8868 non-null float64 128 red_card_per_game_10 8868 non-null float64 129 team_goals_per_game_10 8149 non-null float64 130 points_per_game_10 8868 non-null float64 131 team_conceeded_goals_per_game_10 8149 non-null float64 132 clean_sheet_per_game_10 8868 non-null float64 133 team_goals_per_game_not_played_10 6451 non-null float64 134 clean_sheet_per_game_not_played_10 7658 non-null float64 135 team_conceeded_goals_per_game_not_played_10 6451 non-null float64 136 points_per_game_not_played_10 7658 non-null float64 137 minutes_played_coverage_played_10 8868 non-null float64 138 minutes_played_coverage_all_10 9381 non-null float64 139 points_played_coverage_10 8868 non-null float64 140 points_not_played_coverage_10 7658 non-null float64 141 Injury_10 1042 non-null float64 142 Suspension_10 1064 non-null float64 143 not in squad_10 4910 non-null float64 144 on the bench _10 3552 non-null float64 145 injury_rate_10 1042 non-null float64 146 suspension_rate_10 1064 non-null float64 147 on_the_bench_rate_10 3552 non-null float64 148 not_in_squad_rate_10 4910 non-null float64 149 last_played_game_recency_10 9381 non-null timedelta64[ns] 150 Date_5 9381 non-null int64 151 starting_lineup_5 9381 non-null int32 152 matches_played_flag_5 9381 non-null int32 153 Goals_5 9381 non-null float64 154 Assists_5 9381 non-null float64 155 Own goals_5 9381 non-null float64 156 yellow_card_flag_5 9381 non-null int32 157 second_yellow_card_flag_5 9381 non-null int32 158 red_card_flag_5 9381 non-null int32 159 Minutes played_5 9381 non-null float64 160 substitution_off_flag_5 9381 non-null int32 161 match_played_rate_5 9381 non-null float64 162 rate_subs_off_5 7690 non-null float64 163 match_points_played_5 8254 non-null float64 164 match_points_notplayed_5 6216 non-null float64 165 total_goals_team_5 9238 non-null float64 166 total_goals_team_played_5 6996 non-null float64 167 total_goals_team_conceded_5 9238 non-null float64 168 total_goals_team_conceded_played_5 6996 non-null float64 169 clean_sheet_flg_played_5 8254 non-null float64 170 clean_sheet_flg_notplayed_5 6216 non-null float64 171 lineup_rate_5 8254 non-null float64 172 minutes_per_game_5 8254 non-null float64 173 goals_per_Game_5 8254 non-null float64 174 assists_per_game_5 8254 non-null float64 175 own_goals_per_game_5 8254 non-null float64 176 yellow_cards_per_game_5 8254 non-null float64 177 second_yellow_card_per_game_5 8254 non-null float64 178 red_card_per_game_5 8254 non-null float64 179 team_goals_per_game_5 6996 non-null float64 180 points_per_game_5 8254 non-null float64 181 team_conceeded_goals_per_game_5 6996 non-null float64 182 clean_sheet_per_game_5 8254 non-null float64 183 team_goals_per_game_not_played_5 3876 non-null float64 184 clean_sheet_per_game_not_played_5 6216 non-null float64 185 team_conceeded_goals_per_game_not_played_5 3876 non-null float64 186 points_per_game_not_played_5 6216 non-null float64 187 minutes_played_coverage_played_5 8254 non-null float64 188 minutes_played_coverage_all_5 9381 non-null float64 189 points_played_coverage_5 8254 non-null float64 190 points_not_played_coverage_5 6216 non-null float64 191 Injury_5 655 non-null float64 192 Suspension_5 563 non-null float64 193 not in squad_5 3719 non-null float64 194 on the bench _5 2456 non-null float64 195 injury_rate_5 655 non-null float64 196 suspension_rate_5 563 non-null float64 197 on_the_bench_rate_5 2456 non-null float64 198 not_in_squad_rate_5 3719 non-null float64 199 last_played_game_recency_5 9381 non-null timedelta64[ns] dtypes: float64(168), int32(24), int64(4), timedelta64[ns](4) memory usage: 13.5+ MB
A football player can also play for his national team. National team stats can also be helpful to predict transfer fee. Here the difficult part of this dataset is to get the matches of players, since they can play multiple under levels also like Under 21, Under 19 etc. Here is the link of national matches. I first scraped the clubs in "national_urls_scraper.ipynb" then I looped for different national level clubs and for all players to create dataset.
The remaining part of national stats matches and features are just like club matches.

The dataset pattern is like club matches dateset:
national_stats = pd.read_pickle('C:/Users/YAVUZ/Transfer Fee Project/1.Feature Engineering/National_Stats_Features_All.pkl')
national_stats.info()
<class 'pandas.core.frame.DataFrame'> Index: 6534 entries, 3104833 to 1115385 Data columns (total 200 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 match_date_National_30 6534 non-null int64 1 starting_lineup_National_30 6534 non-null int32 2 matches_played_flag_National_30 6534 non-null int32 3 goals_National_30 6534 non-null float64 4 assists_National_30 6534 non-null float64 5 own_goals_National_30 6534 non-null float64 6 yellow_card_flag_National_30 6534 non-null int32 7 second_yellow_card_flag_National_30 6534 non-null int32 8 red_card_flag_National_30 6534 non-null int32 9 minutes_played_National_30 6534 non-null float64 10 substitution_off_flag_National_30 6534 non-null int32 11 match_played_rate_National_30 6534 non-null float64 12 rate_subs_off_National_30 5843 non-null float64 13 match_points_played_National_30 6272 non-null float64 14 match_points_notplayed_National_30 5946 non-null float64 15 total_goals_team_National_30 6057 non-null float64 16 total_goals_team_played_National_30 5375 non-null float64 17 total_goals_team_conceded_National_30 6057 non-null float64 18 total_goals_team_conceded_played_National_30 5375 non-null float64 19 clean_sheet_flg_played_National_30 6272 non-null float64 20 clean_sheet_flg_notplayed_National_30 5946 non-null float64 21 lineup_rate_National_30 6272 non-null float64 22 minutes_per_game_National_30 6272 non-null float64 23 goals_per_Game_National_30 6272 non-null float64 24 assists_per_game_National_30 6272 non-null float64 25 own_goals_per_game_National_30 6272 non-null float64 26 yellow_cards_per_game_National_30 6272 non-null float64 27 second_yellow_card_per_game_National_30 6272 non-null float64 28 red_card_per_game_National_30 6272 non-null float64 29 team_goals_per_game_National_30 5375 non-null float64 30 points_per_game_National_30 6272 non-null float64 31 team_conceeded_goals_per_game_National_30 5375 non-null float64 32 clean_sheet_per_game_National_30 6272 non-null float64 33 team_goals_per_game_not_played_National_30 5065 non-null float64 34 clean_sheet_per_game_not_played_National_30 5946 non-null float64 35 team_conceeded_goals_per_game_not_played_National_30 5065 non-null float64 36 points_per_game_not_played_National_30 5946 non-null float64 37 minutes_played_coverage_played_National_30 6272 non-null float64 38 minutes_played_coverage_all_National_30 6534 non-null float64 39 points_played_coverage_National_30 6272 non-null float64 40 points_not_played_coverage_National_30 5946 non-null float64 41 Injury_National_30 1352 non-null float64 42 Suspension_National_30 203 non-null float64 43 not in squad_National_30 4852 non-null float64 44 on the bench_National_30 5033 non-null float64 45 injury_rate_National_30 1352 non-null float64 46 suspension_rate_National_30 203 non-null float64 47 on_the_bench_rate_National_30 5033 non-null float64 48 not_in_squad_rate_National_30 4852 non-null float64 49 last_played_game_recency_National_30 6534 non-null timedelta64[ns] 50 match_date_National_20 6534 non-null int64 51 starting_lineup_National_20 6534 non-null int32 52 matches_played_flag_National_20 6534 non-null int32 53 goals_National_20 6534 non-null float64 54 assists_National_20 6534 non-null float64 55 own_goals_National_20 6534 non-null float64 56 yellow_card_flag_National_20 6534 non-null int32 57 second_yellow_card_flag_National_20 6534 non-null int32 58 red_card_flag_National_20 6534 non-null int32 59 minutes_played_National_20 6534 non-null float64 60 substitution_off_flag_National_20 6534 non-null int32 61 match_played_rate_National_20 6534 non-null float64 62 rate_subs_off_National_20 5720 non-null float64 63 match_points_played_National_20 6187 non-null float64 64 match_points_notplayed_National_20 5918 non-null float64 65 total_goals_team_National_20 6055 non-null float64 66 total_goals_team_played_National_20 5190 non-null float64 67 total_goals_team_conceded_National_20 6055 non-null float64 68 total_goals_team_conceded_played_National_20 5190 non-null float64 69 clean_sheet_flg_played_National_20 6187 non-null float64 70 clean_sheet_flg_notplayed_National_20 5918 non-null float64 71 lineup_rate_National_20 6187 non-null float64 72 minutes_per_game_National_20 6187 non-null float64 73 goals_per_Game_National_20 6187 non-null float64 74 assists_per_game_National_20 6187 non-null float64 75 own_goals_per_game_National_20 6187 non-null float64 76 yellow_cards_per_game_National_20 6187 non-null float64 77 second_yellow_card_per_game_National_20 6187 non-null float64 78 red_card_per_game_National_20 6187 non-null float64 79 team_goals_per_game_National_20 5190 non-null float64 80 points_per_game_National_20 6187 non-null float64 81 team_conceeded_goals_per_game_National_20 5190 non-null float64 82 clean_sheet_per_game_National_20 6187 non-null float64 83 team_goals_per_game_not_played_National_20 4852 non-null float64 84 clean_sheet_per_game_not_played_National_20 5918 non-null float64 85 team_conceeded_goals_per_game_not_played_National_20 4852 non-null float64 86 points_per_game_not_played_National_20 5918 non-null float64 87 minutes_played_coverage_played_National_20 6187 non-null float64 88 minutes_played_coverage_all_National_20 6534 non-null float64 89 points_played_coverage_National_20 6187 non-null float64 90 points_not_played_coverage_National_20 5918 non-null float64 91 Injury_National_20 1163 non-null float64 92 Suspension_National_20 168 non-null float64 93 not in squad_National_20 4643 non-null float64 94 on the bench_National_20 4718 non-null float64 95 injury_rate_National_20 1163 non-null float64 96 suspension_rate_National_20 168 non-null float64 97 on_the_bench_rate_National_20 4718 non-null float64 98 not_in_squad_rate_National_20 4643 non-null float64 99 last_played_game_recency_National_20 6534 non-null timedelta64[ns] 100 match_date_National_10 6534 non-null int64 101 starting_lineup_National_10 6534 non-null int32 102 matches_played_flag_National_10 6534 non-null int32 103 goals_National_10 6534 non-null float64 104 assists_National_10 6534 non-null float64 105 own_goals_National_10 6534 non-null float64 106 yellow_card_flag_National_10 6534 non-null int32 107 second_yellow_card_flag_National_10 6534 non-null int32 108 red_card_flag_National_10 6534 non-null int32 109 minutes_played_National_10 6534 non-null float64 110 substitution_off_flag_National_10 6534 non-null int32 111 match_played_rate_National_10 6534 non-null float64 112 rate_subs_off_National_10 5335 non-null float64 113 match_points_played_National_10 5964 non-null float64 114 match_points_notplayed_National_10 5706 non-null float64 115 total_goals_team_National_10 6033 non-null float64 116 total_goals_team_played_National_10 4556 non-null float64 117 total_goals_team_conceded_National_10 6033 non-null float64 118 total_goals_team_conceded_played_National_10 4556 non-null float64 119 clean_sheet_flg_played_National_10 5964 non-null float64 120 clean_sheet_flg_notplayed_National_10 5706 non-null float64 121 lineup_rate_National_10 5964 non-null float64 122 minutes_per_game_National_10 5964 non-null float64 123 goals_per_Game_National_10 5964 non-null float64 124 assists_per_game_National_10 5964 non-null float64 125 own_goals_per_game_National_10 5964 non-null float64 126 yellow_cards_per_game_National_10 5964 non-null float64 127 second_yellow_card_per_game_National_10 5964 non-null float64 128 red_card_per_game_National_10 5964 non-null float64 129 team_goals_per_game_National_10 4556 non-null float64 130 points_per_game_National_10 5964 non-null float64 131 team_conceeded_goals_per_game_National_10 4556 non-null float64 132 clean_sheet_per_game_National_10 5964 non-null float64 133 team_goals_per_game_not_played_National_10 4007 non-null float64 134 clean_sheet_per_game_not_played_National_10 5706 non-null float64 135 team_conceeded_goals_per_game_not_played_National_10 4007 non-null float64 136 points_per_game_not_played_National_10 5706 non-null float64 137 minutes_played_coverage_played_National_10 5964 non-null float64 138 minutes_played_coverage_all_National_10 6534 non-null float64 139 points_played_coverage_National_10 5964 non-null float64 140 points_not_played_coverage_National_10 5706 non-null float64 141 Injury_National_10 724 non-null float64 142 Suspension_National_10 109 non-null float64 143 not in squad_National_10 3919 non-null float64 144 on the bench_National_10 4031 non-null float64 145 injury_rate_National_10 724 non-null float64 146 suspension_rate_National_10 109 non-null float64 147 on_the_bench_rate_National_10 4031 non-null float64 148 not_in_squad_rate_National_10 3919 non-null float64 149 last_played_game_recency_National_10 6534 non-null timedelta64[ns] 150 match_date_National_5 6534 non-null int64 151 starting_lineup_National_5 6534 non-null int32 152 matches_played_flag_National_5 6534 non-null int32 153 goals_National_5 6534 non-null float64 154 assists_National_5 6534 non-null float64 155 own_goals_National_5 6534 non-null float64 156 yellow_card_flag_National_5 6534 non-null int32 157 second_yellow_card_flag_National_5 6534 non-null int32 158 red_card_flag_National_5 6534 non-null int32 159 minutes_played_National_5 6534 non-null float64 160 substitution_off_flag_National_5 6534 non-null int32 161 match_played_rate_National_5 6534 non-null float64 162 rate_subs_off_National_5 4691 non-null float64 163 match_points_played_National_5 5572 non-null float64 164 match_points_notplayed_National_5 5079 non-null float64 165 total_goals_team_National_5 5803 non-null float64 166 total_goals_team_played_National_5 3500 non-null float64 167 total_goals_team_conceded_National_5 5803 non-null float64 168 total_goals_team_conceded_played_National_5 3500 non-null float64 169 clean_sheet_flg_played_National_5 5572 non-null float64 170 clean_sheet_flg_notplayed_National_5 5079 non-null float64 171 lineup_rate_National_5 5572 non-null float64 172 minutes_per_game_National_5 5572 non-null float64 173 goals_per_Game_National_5 5572 non-null float64 174 assists_per_game_National_5 5572 non-null float64 175 own_goals_per_game_National_5 5572 non-null float64 176 yellow_cards_per_game_National_5 5572 non-null float64 177 second_yellow_card_per_game_National_5 5572 non-null float64 178 red_card_per_game_National_5 5572 non-null float64 179 team_goals_per_game_National_5 3500 non-null float64 180 points_per_game_National_5 5572 non-null float64 181 team_conceeded_goals_per_game_National_5 3500 non-null float64 182 clean_sheet_per_game_National_5 5572 non-null float64 183 team_goals_per_game_not_played_National_5 2373 non-null float64 184 clean_sheet_per_game_not_played_National_5 5079 non-null float64 185 team_conceeded_goals_per_game_not_played_National_5 2373 non-null float64 186 points_per_game_not_played_National_5 5079 non-null float64 187 minutes_played_coverage_played_National_5 5572 non-null float64 188 minutes_played_coverage_all_National_5 6534 non-null float64 189 points_played_coverage_National_5 5572 non-null float64 190 points_not_played_coverage_National_5 5079 non-null float64 191 Injury_National_5 390 non-null float64 192 Suspension_National_5 62 non-null float64 193 not in squad_National_5 2839 non-null float64 194 on the bench_National_5 3191 non-null float64 195 injury_rate_National_5 390 non-null float64 196 suspension_rate_National_5 62 non-null float64 197 on_the_bench_rate_National_5 3191 non-null float64 198 not_in_squad_rate_National_5 2839 non-null float64 199 last_played_game_recency_National_5 6534 non-null timedelta64[ns] dtypes: float64(168), int32(24), int64(4), timedelta64[ns](4) memory usage: 9.4+ MB
Finally, achievements are also very important for predicting transfer fee. The fee can increase if the player wins top goal scorer awards, or cups. I decided to scrape achievements from the link
In Feature engineering folder I created number of achievements and number of distinct achievements of a player as features to be used in the prediction model.

The data pattern for achievement dataset is:
achievements = pd.read_pickle('C:/Users/YAVUZ/Transfer Fee Project/1.Feature Engineering/achievement_features.pkl')
achievements.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 4199 entries, 0 to 4198 Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Transfer_id 4199 non-null object 1 Total_Achievements 4199 non-null int64 2 Distinct_Achievements 4199 non-null int64 dtypes: int64(2), object(1) memory usage: 98.5+ KB
#Merge datasets
df = transfer_detail.merge(profile, on=['Transfer_id'], how='left')
df=df.merge(achievements, on=['Transfer_id'], how='left')
df=df.merge(national_stats, on=['Transfer_id'], how='left')
df=df.merge(stats, on=['Transfer_id'], how='left')
#import libraries
import pandas as pd
import math
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from lightgbm import LGBMRegressor
from sklearn.metrics import mean_absolute_error,mean_squared_error,r2_score
from math import sqrt
from sklearn.model_selection import GridSearchCV
import plotly.express as px
from datetime import timedelta, date
import datetime
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
pd.set_option('display.float_format', lambda x: '%.3f' % x)
Let's explore the dataset a little bit.First i will take head,tail and describe for the dataframe. Then, I will ask 4 questions to the data and get the answers of each by visualizing.
df.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 9499 entries, 0 to 9498 Data columns (total 440 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Transfer_Link_x 9499 non-null object 1 Transfer_id 9499 non-null object 2 id_x 9499 non-null object 3 NOF_Transfers 9499 non-null object 4 Avg_Market_Value 7729 non-null object 5 Tot_Fee_Loan_Fee_Exc 9499 non-null object 6 Avg_Fee 4166 non-null object 7 Tot_Fee_Loan_Fee_Inc 9499 non-null object 8 Age_at_Time_of_transfer 9499 non-null object 9 Contract_Left_at_Time_of_transfer 7496 non-null object 10 Market_Value_at_Time_of_transfer 9499 non-null object 11 Team_Left_Tier 9447 non-null object 12 Team_Joined_Tier 9447 non-null object 13 Team_Left_League 9447 non-null object 14 Team_Joined_League 9447 non-null object 15 Fee 9499 non-null object 16 Transfer_Season 9499 non-null object 17 Transfer_Date_x 9499 non-null object 18 Team_Left 9499 non-null object 19 Team_Joined 9499 non-null object 20 Country_Left 9141 non-null object 21 Country_Joined 9371 non-null object 22 Country_Change_Flag 9499 non-null object 23 League_Change_Flag 9499 non-null object 24 League_Tier_Up_Flag 9499 non-null object 25 League_Tier_Down_Flag 9499 non-null object 26 Transfer_Link_y 9499 non-null object 27 id_y 9499 non-null object 28 Transfer_Date_y 9499 non-null object 29 Date_of_birth 9499 non-null object 30 Place_of_birth 9499 non-null object 31 Height 9499 non-null object 32 Citizenship 9499 non-null object 33 Position 9499 non-null object 34 Foot 9499 non-null object 35 Main_position 9499 non-null object 36 other_position1 9499 non-null object 37 other_position2 9499 non-null object 38 Total_Achievements 4199 non-null float64 39 Distinct_Achievements 4199 non-null float64 40 match_date_National_30 6534 non-null float64 41 starting_lineup_National_30 6534 non-null float64 42 matches_played_flag_National_30 6534 non-null float64 43 goals_National_30 6534 non-null float64 44 assists_National_30 6534 non-null float64 45 own_goals_National_30 6534 non-null float64 46 yellow_card_flag_National_30 6534 non-null float64 47 second_yellow_card_flag_National_30 6534 non-null float64 48 red_card_flag_National_30 6534 non-null float64 49 minutes_played_National_30 6534 non-null float64 50 substitution_off_flag_National_30 6534 non-null float64 51 match_played_rate_National_30 6534 non-null float64 52 rate_subs_off_National_30 5843 non-null float64 53 match_points_played_National_30 6272 non-null float64 54 match_points_notplayed_National_30 5946 non-null float64 55 total_goals_team_National_30 6057 non-null float64 56 total_goals_team_played_National_30 5375 non-null float64 57 total_goals_team_conceded_National_30 6057 non-null float64 58 total_goals_team_conceded_played_National_30 5375 non-null float64 59 clean_sheet_flg_played_National_30 6272 non-null float64 60 clean_sheet_flg_notplayed_National_30 5946 non-null float64 61 lineup_rate_National_30 6272 non-null float64 62 minutes_per_game_National_30 6272 non-null float64 63 goals_per_Game_National_30 6272 non-null float64 64 assists_per_game_National_30 6272 non-null float64 65 own_goals_per_game_National_30 6272 non-null float64 66 yellow_cards_per_game_National_30 6272 non-null float64 67 second_yellow_card_per_game_National_30 6272 non-null float64 68 red_card_per_game_National_30 6272 non-null float64 69 team_goals_per_game_National_30 5375 non-null float64 70 points_per_game_National_30 6272 non-null float64 71 team_conceeded_goals_per_game_National_30 5375 non-null float64 72 clean_sheet_per_game_National_30 6272 non-null float64 73 team_goals_per_game_not_played_National_30 5065 non-null float64 74 clean_sheet_per_game_not_played_National_30 5946 non-null float64 75 team_conceeded_goals_per_game_not_played_National_30 5065 non-null float64 76 points_per_game_not_played_National_30 5946 non-null float64 77 minutes_played_coverage_played_National_30 6272 non-null float64 78 minutes_played_coverage_all_National_30 6534 non-null float64 79 points_played_coverage_National_30 6272 non-null float64 80 points_not_played_coverage_National_30 5946 non-null float64 81 Injury_National_30 1352 non-null float64 82 Suspension_National_30 203 non-null float64 83 not in squad_National_30 4852 non-null float64 84 on the bench_National_30 5033 non-null float64 85 injury_rate_National_30 1352 non-null float64 86 suspension_rate_National_30 203 non-null float64 87 on_the_bench_rate_National_30 5033 non-null float64 88 not_in_squad_rate_National_30 4852 non-null float64 89 last_played_game_recency_National_30 6534 non-null timedelta64[ns] 90 match_date_National_20 6534 non-null float64 91 starting_lineup_National_20 6534 non-null float64 92 matches_played_flag_National_20 6534 non-null float64 93 goals_National_20 6534 non-null float64 94 assists_National_20 6534 non-null float64 95 own_goals_National_20 6534 non-null float64 96 yellow_card_flag_National_20 6534 non-null float64 97 second_yellow_card_flag_National_20 6534 non-null float64 98 red_card_flag_National_20 6534 non-null float64 99 minutes_played_National_20 6534 non-null float64 100 substitution_off_flag_National_20 6534 non-null float64 101 match_played_rate_National_20 6534 non-null float64 102 rate_subs_off_National_20 5720 non-null float64 103 match_points_played_National_20 6187 non-null float64 104 match_points_notplayed_National_20 5918 non-null float64 105 total_goals_team_National_20 6055 non-null float64 106 total_goals_team_played_National_20 5190 non-null float64 107 total_goals_team_conceded_National_20 6055 non-null float64 108 total_goals_team_conceded_played_National_20 5190 non-null float64 109 clean_sheet_flg_played_National_20 6187 non-null float64 110 clean_sheet_flg_notplayed_National_20 5918 non-null float64 111 lineup_rate_National_20 6187 non-null float64 112 minutes_per_game_National_20 6187 non-null float64 113 goals_per_Game_National_20 6187 non-null float64 114 assists_per_game_National_20 6187 non-null float64 115 own_goals_per_game_National_20 6187 non-null float64 116 yellow_cards_per_game_National_20 6187 non-null float64 117 second_yellow_card_per_game_National_20 6187 non-null float64 118 red_card_per_game_National_20 6187 non-null float64 119 team_goals_per_game_National_20 5190 non-null float64 120 points_per_game_National_20 6187 non-null float64 121 team_conceeded_goals_per_game_National_20 5190 non-null float64 122 clean_sheet_per_game_National_20 6187 non-null float64 123 team_goals_per_game_not_played_National_20 4852 non-null float64 124 clean_sheet_per_game_not_played_National_20 5918 non-null float64 125 team_conceeded_goals_per_game_not_played_National_20 4852 non-null float64 126 points_per_game_not_played_National_20 5918 non-null float64 127 minutes_played_coverage_played_National_20 6187 non-null float64 128 minutes_played_coverage_all_National_20 6534 non-null float64 129 points_played_coverage_National_20 6187 non-null float64 130 points_not_played_coverage_National_20 5918 non-null float64 131 Injury_National_20 1163 non-null float64 132 Suspension_National_20 168 non-null float64 133 not in squad_National_20 4643 non-null float64 134 on the bench_National_20 4718 non-null float64 135 injury_rate_National_20 1163 non-null float64 136 suspension_rate_National_20 168 non-null float64 137 on_the_bench_rate_National_20 4718 non-null float64 138 not_in_squad_rate_National_20 4643 non-null float64 139 last_played_game_recency_National_20 6534 non-null timedelta64[ns] 140 match_date_National_10 6534 non-null float64 141 starting_lineup_National_10 6534 non-null float64 142 matches_played_flag_National_10 6534 non-null float64 143 goals_National_10 6534 non-null float64 144 assists_National_10 6534 non-null float64 145 own_goals_National_10 6534 non-null float64 146 yellow_card_flag_National_10 6534 non-null float64 147 second_yellow_card_flag_National_10 6534 non-null float64 148 red_card_flag_National_10 6534 non-null float64 149 minutes_played_National_10 6534 non-null float64 150 substitution_off_flag_National_10 6534 non-null float64 151 match_played_rate_National_10 6534 non-null float64 152 rate_subs_off_National_10 5335 non-null float64 153 match_points_played_National_10 5964 non-null float64 154 match_points_notplayed_National_10 5706 non-null float64 155 total_goals_team_National_10 6033 non-null float64 156 total_goals_team_played_National_10 4556 non-null float64 157 total_goals_team_conceded_National_10 6033 non-null float64 158 total_goals_team_conceded_played_National_10 4556 non-null float64 159 clean_sheet_flg_played_National_10 5964 non-null float64 160 clean_sheet_flg_notplayed_National_10 5706 non-null float64 161 lineup_rate_National_10 5964 non-null float64 162 minutes_per_game_National_10 5964 non-null float64 163 goals_per_Game_National_10 5964 non-null float64 164 assists_per_game_National_10 5964 non-null float64 165 own_goals_per_game_National_10 5964 non-null float64 166 yellow_cards_per_game_National_10 5964 non-null float64 167 second_yellow_card_per_game_National_10 5964 non-null float64 168 red_card_per_game_National_10 5964 non-null float64 169 team_goals_per_game_National_10 4556 non-null float64 170 points_per_game_National_10 5964 non-null float64 171 team_conceeded_goals_per_game_National_10 4556 non-null float64 172 clean_sheet_per_game_National_10 5964 non-null float64 173 team_goals_per_game_not_played_National_10 4007 non-null float64 174 clean_sheet_per_game_not_played_National_10 5706 non-null float64 175 team_conceeded_goals_per_game_not_played_National_10 4007 non-null float64 176 points_per_game_not_played_National_10 5706 non-null float64 177 minutes_played_coverage_played_National_10 5964 non-null float64 178 minutes_played_coverage_all_National_10 6534 non-null float64 179 points_played_coverage_National_10 5964 non-null float64 180 points_not_played_coverage_National_10 5706 non-null float64 181 Injury_National_10 724 non-null float64 182 Suspension_National_10 109 non-null float64 183 not in squad_National_10 3919 non-null float64 184 on the bench_National_10 4031 non-null float64 185 injury_rate_National_10 724 non-null float64 186 suspension_rate_National_10 109 non-null float64 187 on_the_bench_rate_National_10 4031 non-null float64 188 not_in_squad_rate_National_10 3919 non-null float64 189 last_played_game_recency_National_10 6534 non-null timedelta64[ns] 190 match_date_National_5 6534 non-null float64 191 starting_lineup_National_5 6534 non-null float64 192 matches_played_flag_National_5 6534 non-null float64 193 goals_National_5 6534 non-null float64 194 assists_National_5 6534 non-null float64 195 own_goals_National_5 6534 non-null float64 196 yellow_card_flag_National_5 6534 non-null float64 197 second_yellow_card_flag_National_5 6534 non-null float64 198 red_card_flag_National_5 6534 non-null float64 199 minutes_played_National_5 6534 non-null float64 200 substitution_off_flag_National_5 6534 non-null float64 201 match_played_rate_National_5 6534 non-null float64 202 rate_subs_off_National_5 4691 non-null float64 203 match_points_played_National_5 5572 non-null float64 204 match_points_notplayed_National_5 5079 non-null float64 205 total_goals_team_National_5 5803 non-null float64 206 total_goals_team_played_National_5 3500 non-null float64 207 total_goals_team_conceded_National_5 5803 non-null float64 208 total_goals_team_conceded_played_National_5 3500 non-null float64 209 clean_sheet_flg_played_National_5 5572 non-null float64 210 clean_sheet_flg_notplayed_National_5 5079 non-null float64 211 lineup_rate_National_5 5572 non-null float64 212 minutes_per_game_National_5 5572 non-null float64 213 goals_per_Game_National_5 5572 non-null float64 214 assists_per_game_National_5 5572 non-null float64 215 own_goals_per_game_National_5 5572 non-null float64 216 yellow_cards_per_game_National_5 5572 non-null float64 217 second_yellow_card_per_game_National_5 5572 non-null float64 218 red_card_per_game_National_5 5572 non-null float64 219 team_goals_per_game_National_5 3500 non-null float64 220 points_per_game_National_5 5572 non-null float64 221 team_conceeded_goals_per_game_National_5 3500 non-null float64 222 clean_sheet_per_game_National_5 5572 non-null float64 223 team_goals_per_game_not_played_National_5 2373 non-null float64 224 clean_sheet_per_game_not_played_National_5 5079 non-null float64 225 team_conceeded_goals_per_game_not_played_National_5 2373 non-null float64 226 points_per_game_not_played_National_5 5079 non-null float64 227 minutes_played_coverage_played_National_5 5572 non-null float64 228 minutes_played_coverage_all_National_5 6534 non-null float64 229 points_played_coverage_National_5 5572 non-null float64 230 points_not_played_coverage_National_5 5079 non-null float64 231 Injury_National_5 390 non-null float64 232 Suspension_National_5 62 non-null float64 233 not in squad_National_5 2839 non-null float64 234 on the bench_National_5 3191 non-null float64 235 injury_rate_National_5 390 non-null float64 236 suspension_rate_National_5 62 non-null float64 237 on_the_bench_rate_National_5 3191 non-null float64 238 not_in_squad_rate_National_5 2839 non-null float64 239 last_played_game_recency_National_5 6534 non-null timedelta64[ns] 240 Date_30 9381 non-null float64 241 starting_lineup_30 9381 non-null float64 242 matches_played_flag_30 9381 non-null float64 243 Goals_30 9381 non-null float64 244 Assists_30 9381 non-null float64 245 Own goals_30 9381 non-null float64 246 yellow_card_flag_30 9381 non-null float64 247 second_yellow_card_flag_30 9381 non-null float64 248 red_card_flag_30 9381 non-null float64 249 Minutes played_30 9381 non-null float64 250 substitution_off_flag_30 9381 non-null float64 251 match_played_rate_30 9381 non-null float64 252 rate_subs_off_30 9208 non-null float64 253 match_points_played_30 9282 non-null float64 254 match_points_notplayed_30 9098 non-null float64 255 total_goals_team_30 9287 non-null float64 256 total_goals_team_played_30 9002 non-null float64 257 total_goals_team_conceded_30 9287 non-null float64 258 total_goals_team_conceded_played_30 9002 non-null float64 259 clean_sheet_flg_played_30 9282 non-null float64 260 clean_sheet_flg_notplayed_30 9098 non-null float64 261 lineup_rate_30 9282 non-null float64 262 minutes_per_game_30 9282 non-null float64 263 goals_per_Game_30 9282 non-null float64 264 assists_per_game_30 9282 non-null float64 265 own_goals_per_game_30 9282 non-null float64 266 yellow_cards_per_game_30 9282 non-null float64 267 second_yellow_card_per_game_30 9282 non-null float64 268 red_card_per_game_30 9282 non-null float64 269 team_goals_per_game_30 9002 non-null float64 270 points_per_game_30 9282 non-null float64 271 team_conceeded_goals_per_game_30 9002 non-null float64 272 clean_sheet_per_game_30 9282 non-null float64 273 team_goals_per_game_not_played_30 8724 non-null float64 274 clean_sheet_per_game_not_played_30 9098 non-null float64 275 team_conceeded_goals_per_game_not_played_30 8724 non-null float64 276 points_per_game_not_played_30 9098 non-null float64 277 minutes_played_coverage_played_30 9282 non-null float64 278 minutes_played_coverage_all_30 9381 non-null float64 279 points_played_coverage_30 9282 non-null float64 280 points_not_played_coverage_30 9098 non-null float64 281 Injury_30 2108 non-null float64 282 Suspension_30 2218 non-null float64 283 not in squad_30 7288 non-null float64 284 on the bench _30 6088 non-null float64 285 injury_rate_30 2108 non-null float64 286 suspension_rate_30 2218 non-null float64 287 on_the_bench_rate_30 6088 non-null float64 288 not_in_squad_rate_30 7288 non-null float64 289 last_played_game_recency_30 9381 non-null timedelta64[ns] 290 Date_20 9381 non-null float64 291 starting_lineup_20 9381 non-null float64 292 matches_played_flag_20 9381 non-null float64 293 Goals_20 9381 non-null float64 294 Assists_20 9381 non-null float64 295 Own goals_20 9381 non-null float64 296 yellow_card_flag_20 9381 non-null float64 297 second_yellow_card_flag_20 9381 non-null float64 298 red_card_flag_20 9381 non-null float64 299 Minutes played_20 9381 non-null float64 300 substitution_off_flag_20 9381 non-null float64 301 match_played_rate_20 9381 non-null float64 302 rate_subs_off_20 9046 non-null float64 303 match_points_played_20 9180 non-null float64 304 match_points_notplayed_20 8747 non-null float64 305 total_goals_team_20 9268 non-null float64 306 total_goals_team_played_20 8759 non-null float64 307 total_goals_team_conceded_20 9268 non-null float64 308 total_goals_team_conceded_played_20 8759 non-null float64 309 clean_sheet_flg_played_20 9180 non-null float64 310 clean_sheet_flg_notplayed_20 8747 non-null float64 311 lineup_rate_20 9180 non-null float64 312 minutes_per_game_20 9180 non-null float64 313 goals_per_Game_20 9180 non-null float64 314 assists_per_game_20 9180 non-null float64 315 own_goals_per_game_20 9180 non-null float64 316 yellow_cards_per_game_20 9180 non-null float64 317 second_yellow_card_per_game_20 9180 non-null float64 318 red_card_per_game_20 9180 non-null float64 319 team_goals_per_game_20 8759 non-null float64 320 points_per_game_20 9180 non-null float64 321 team_conceeded_goals_per_game_20 8759 non-null float64 322 clean_sheet_per_game_20 9180 non-null float64 323 team_goals_per_game_not_played_20 8133 non-null float64 324 clean_sheet_per_game_not_played_20 8747 non-null float64 325 team_conceeded_goals_per_game_not_played_20 8133 non-null float64 326 points_per_game_not_played_20 8747 non-null float64 327 minutes_played_coverage_played_20 9180 non-null float64 328 minutes_played_coverage_all_20 9381 non-null float64 329 points_played_coverage_20 9180 non-null float64 330 points_not_played_coverage_20 8747 non-null float64 331 Injury_20 1652 non-null float64 332 Suspension_20 1745 non-null float64 333 not in squad_20 6358 non-null float64 334 on the bench _20 4990 non-null float64 335 injury_rate_20 1652 non-null float64 336 suspension_rate_20 1745 non-null float64 337 on_the_bench_rate_20 4990 non-null float64 338 not_in_squad_rate_20 6358 non-null float64 339 last_played_game_recency_20 9381 non-null timedelta64[ns] 340 Date_10 9381 non-null float64 341 starting_lineup_10 9381 non-null float64 342 matches_played_flag_10 9381 non-null float64 343 Goals_10 9381 non-null float64 344 Assists_10 9381 non-null float64 345 Own goals_10 9381 non-null float64 346 yellow_card_flag_10 9381 non-null float64 347 second_yellow_card_flag_10 9381 non-null float64 348 red_card_flag_10 9381 non-null float64 349 Minutes played_10 9381 non-null float64 350 substitution_off_flag_10 9381 non-null float64 351 match_played_rate_10 9381 non-null float64 352 rate_subs_off_10 8523 non-null float64 353 match_points_played_10 8868 non-null float64 354 match_points_notplayed_10 7658 non-null float64 355 total_goals_team_10 9261 non-null float64 356 total_goals_team_played_10 8149 non-null float64 357 total_goals_team_conceded_10 9261 non-null float64 358 total_goals_team_conceded_played_10 8149 non-null float64 359 clean_sheet_flg_played_10 8868 non-null float64 360 clean_sheet_flg_notplayed_10 7658 non-null float64 361 lineup_rate_10 8868 non-null float64 362 minutes_per_game_10 8868 non-null float64 363 goals_per_Game_10 8868 non-null float64 364 assists_per_game_10 8868 non-null float64 365 own_goals_per_game_10 8868 non-null float64 366 yellow_cards_per_game_10 8868 non-null float64 367 second_yellow_card_per_game_10 8868 non-null float64 368 red_card_per_game_10 8868 non-null float64 369 team_goals_per_game_10 8149 non-null float64 370 points_per_game_10 8868 non-null float64 371 team_conceeded_goals_per_game_10 8149 non-null float64 372 clean_sheet_per_game_10 8868 non-null float64 373 team_goals_per_game_not_played_10 6451 non-null float64 374 clean_sheet_per_game_not_played_10 7658 non-null float64 375 team_conceeded_goals_per_game_not_played_10 6451 non-null float64 376 points_per_game_not_played_10 7658 non-null float64 377 minutes_played_coverage_played_10 8868 non-null float64 378 minutes_played_coverage_all_10 9381 non-null float64 379 points_played_coverage_10 8868 non-null float64 380 points_not_played_coverage_10 7658 non-null float64 381 Injury_10 1042 non-null float64 382 Suspension_10 1064 non-null float64 383 not in squad_10 4910 non-null float64 384 on the bench _10 3552 non-null float64 385 injury_rate_10 1042 non-null float64 386 suspension_rate_10 1064 non-null float64 387 on_the_bench_rate_10 3552 non-null float64 388 not_in_squad_rate_10 4910 non-null float64 389 last_played_game_recency_10 9381 non-null timedelta64[ns] 390 Date_5 9381 non-null float64 391 starting_lineup_5 9381 non-null float64 392 matches_played_flag_5 9381 non-null float64 393 Goals_5 9381 non-null float64 394 Assists_5 9381 non-null float64 395 Own goals_5 9381 non-null float64 396 yellow_card_flag_5 9381 non-null float64 397 second_yellow_card_flag_5 9381 non-null float64 398 red_card_flag_5 9381 non-null float64 399 Minutes played_5 9381 non-null float64 400 substitution_off_flag_5 9381 non-null float64 401 match_played_rate_5 9381 non-null float64 402 rate_subs_off_5 7690 non-null float64 403 match_points_played_5 8254 non-null float64 404 match_points_notplayed_5 6216 non-null float64 405 total_goals_team_5 9238 non-null float64 406 total_goals_team_played_5 6996 non-null float64 407 total_goals_team_conceded_5 9238 non-null float64 408 total_goals_team_conceded_played_5 6996 non-null float64 409 clean_sheet_flg_played_5 8254 non-null float64 410 clean_sheet_flg_notplayed_5 6216 non-null float64 411 lineup_rate_5 8254 non-null float64 412 minutes_per_game_5 8254 non-null float64 413 goals_per_Game_5 8254 non-null float64 414 assists_per_game_5 8254 non-null float64 415 own_goals_per_game_5 8254 non-null float64 416 yellow_cards_per_game_5 8254 non-null float64 417 second_yellow_card_per_game_5 8254 non-null float64 418 red_card_per_game_5 8254 non-null float64 419 team_goals_per_game_5 6996 non-null float64 420 points_per_game_5 8254 non-null float64 421 team_conceeded_goals_per_game_5 6996 non-null float64 422 clean_sheet_per_game_5 8254 non-null float64 423 team_goals_per_game_not_played_5 3876 non-null float64 424 clean_sheet_per_game_not_played_5 6216 non-null float64 425 team_conceeded_goals_per_game_not_played_5 3876 non-null float64 426 points_per_game_not_played_5 6216 non-null float64 427 minutes_played_coverage_played_5 8254 non-null float64 428 minutes_played_coverage_all_5 9381 non-null float64 429 points_played_coverage_5 8254 non-null float64 430 points_not_played_coverage_5 6216 non-null float64 431 Injury_5 655 non-null float64 432 Suspension_5 563 non-null float64 433 not in squad_5 3719 non-null float64 434 on the bench _5 2456 non-null float64 435 injury_rate_5 655 non-null float64 436 suspension_rate_5 563 non-null float64 437 on_the_bench_rate_5 2456 non-null float64 438 not_in_squad_rate_5 3719 non-null float64 439 last_played_game_recency_5 9381 non-null timedelta64[ns] dtypes: float64(394), object(38), timedelta64[ns](8) memory usage: 32.0+ MB
df.head()
| Transfer_Link_x | Transfer_id | id_x | NOF_Transfers | Avg_Market_Value | Tot_Fee_Loan_Fee_Exc | Avg_Fee | Tot_Fee_Loan_Fee_Inc | Age_at_Time_of_transfer | Contract_Left_at_Time_of_transfer | ... | points_not_played_coverage_5 | Injury_5 | Suspension_5 | not in squad_5 | on the bench _5 | injury_rate_5 | suspension_rate_5 | on_the_bench_rate_5 | not_in_squad_rate_5 | last_played_game_recency_5 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | /jumplist/transfers/spieler/7161/transfer_id/1... | 1391587 | 7161 | 7 | 2137500.000 | 0 | NaN | 200000 | 23 years 05 months 18 days | 02 Years 05 Months 29 Days (Jun 30, 2018) | ... | 0.670 | nan | nan | nan | 3.000 | nan | nan | 0.600 | nan | 27 days |
| 1 | /jumplist/transfers/spieler/147266/transfer_id... | 1381390 | 147266 | 4 | 1283333.333 | 0 | NaN | 0 | 25 years 09 months 03 days | NaN | ... | nan | nan | nan | nan | nan | nan | nan | nan | nan | 57 days |
| 2 | /jumplist/transfers/spieler/211655/transfer_id... | 1389543 | 211655 | 1 | NaN | 320000 | 320000.000 | 320000 | 26 years 04 months 12 days | 01 Years 11 Months 29 Days (Dec 31, 2017) | ... | nan | nan | nan | nan | nan | nan | nan | nan | nan | 104 days |
| 3 | /jumplist/transfers/spieler/81820/transfer_id/... | 1386344 | 81820 | 2 | 100000.000 | 2350000 | 1175000.000 | 2350000 | 29 years 04 months 00 days | 11 Months 29 Days (Dec 31, 2016) | ... | 0.330 | nan | nan | 2.000 | 1.000 | nan | nan | 0.200 | 0.400 | 102 days |
| 4 | /jumplist/transfers/spieler/334042/transfer_id... | 1381389 | 334042 | 1 | NaN | 0 | NaN | 0 | 19 years 09 months 08 days | NaN | ... | nan | nan | nan | nan | nan | nan | nan | nan | nan | 75 days |
5 rows × 440 columns
df.tail()
| Transfer_Link_x | Transfer_id | id_x | NOF_Transfers | Avg_Market_Value | Tot_Fee_Loan_Fee_Exc | Avg_Fee | Tot_Fee_Loan_Fee_Inc | Age_at_Time_of_transfer | Contract_Left_at_Time_of_transfer | ... | points_not_played_coverage_5 | Injury_5 | Suspension_5 | not in squad_5 | on the bench _5 | injury_rate_5 | suspension_rate_5 | on_the_bench_rate_5 | not_in_squad_rate_5 | last_played_game_recency_5 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 9494 | /jumplist/transfers/spieler/262300/transfer_id... | 3104833 | 262300 | 5 | 250000.000 | 68000 | 68000.000 | 68000 | 24 years 04 months 02 days | NaN | ... | nan | nan | nan | nan | nan | nan | nan | nan | nan | 100 days |
| 9495 | /jumplist/transfers/spieler/60761/transfer_id/... | 3104424 | 60761 | 4 | 916666.667 | 1200000 | 1200000.000 | 1200000 | 28 years 03 months 25 days | 01 Years 08 Months 14 Days (Jun 30, 2022) | ... | 0.670 | nan | nan | 2.000 | 1.000 | nan | nan | 0.200 | 0.400 | 232 days |
| 9496 | /jumplist/transfers/spieler/650245/transfer_id... | 3104083 | 650245 | 1 | 25000.000 | 0 | NaN | 0 | 19 years 05 months 19 days | NaN | ... | 0.000 | nan | nan | 1.000 | nan | nan | nan | nan | 0.200 | 369 days |
| 9497 | /jumplist/transfers/spieler/333680/transfer_id... | 3105139 | 333680 | 3 | 200000.000 | 0 | NaN | 0 | 25 years 02 months 23 days | 02 Years 07 Months 14 Days (May 31, 2023) | ... | 0.000 | nan | nan | 1.000 | nan | nan | nan | nan | 0.200 | 255 days |
| 9498 | /jumplist/transfers/spieler/447112/transfer_id... | 3105032 | 447112 | 1 | NaN | 0 | NaN | 0 | 22 years 09 months 02 days | 08 Months 13 Days (Jun 30, 2021) | ... | 0.000 | nan | nan | nan | 1.000 | nan | nan | 0.200 | nan | 339 days |
5 rows × 440 columns
df.describe()
| Total_Achievements | Distinct_Achievements | match_date_National_30 | starting_lineup_National_30 | matches_played_flag_National_30 | goals_National_30 | assists_National_30 | own_goals_National_30 | yellow_card_flag_National_30 | second_yellow_card_flag_National_30 | ... | points_not_played_coverage_5 | Injury_5 | Suspension_5 | not in squad_5 | on the bench _5 | injury_rate_5 | suspension_rate_5 | on_the_bench_rate_5 | not_in_squad_rate_5 | last_played_game_recency_5 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 4199.000 | 4199.000 | 6534.000 | 6534.000 | 6534.000 | 6534.000 | 6534.000 | 6534.000 | 6534.000 | 6534.000 | ... | 6216.000 | 655.000 | 563.000 | 3719.000 | 2456.000 | 655.000 | 563.000 | 2456.000 | 3719.000 | 9381 |
| mean | 2.881 | 2.101 | 20.697 | 7.839 | 10.535 | 1.456 | 0.795 | 0.017 | 1.038 | 0.026 | ... | 0.473 | 2.643 | 1.272 | 2.136 | 1.795 | 0.529 | 0.254 | 0.360 | 0.428 | 79 days 08:39:26.997121842 |
| std | 3.061 | 1.580 | 10.674 | 6.674 | 7.515 | 2.519 | 1.449 | 0.131 | 1.420 | 0.164 | ... | 0.347 | 1.507 | 0.806 | 1.390 | 1.085 | 0.301 | 0.161 | 0.218 | 0.278 | 56 days 05:10:28.867578256 |
| min | 1.000 | 1.000 | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ... | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.200 | 0.200 | 0.200 | 0.200 | 3 days 00:00:00 |
| 25% | 1.000 | 1.000 | 11.000 | 2.000 | 4.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ... | 0.170 | 1.000 | 1.000 | 1.000 | 1.000 | 0.200 | 0.200 | 0.200 | 0.200 | 50 days 00:00:00 |
| 50% | 2.000 | 2.000 | 27.000 | 6.000 | 10.000 | 0.000 | 0.000 | 0.000 | 1.000 | 0.000 | ... | 0.470 | 2.000 | 1.000 | 2.000 | 1.000 | 0.400 | 0.200 | 0.200 | 0.400 | 71 days 00:00:00 |
| 75% | 3.000 | 3.000 | 30.000 | 12.000 | 16.000 | 2.000 | 1.000 | 0.000 | 2.000 | 0.000 | ... | 0.750 | 4.000 | 1.000 | 3.000 | 2.000 | 0.800 | 0.200 | 0.400 | 0.600 | 95 days 00:00:00 |
| max | 71.000 | 19.000 | 30.000 | 30.000 | 30.000 | 25.000 | 18.000 | 3.000 | 11.000 | 2.000 | ... | 1.000 | 5.000 | 5.000 | 5.000 | 5.000 | 1.000 | 1.000 | 1.000 | 1.000 | 909 days 00:00:00 |
8 rows × 402 columns
season_count=df.Transfer_Season.value_counts()
#Horizontal bar plot
ax=season_count.plot.barh(figsize=(10,5), fontsize=11,color='darkblue')
ax.set_alpha(0.1)
ax.set_title("Transfer Fee & Season", fontsize=13)
ax.set_ylabel("Season", fontsize=13);
plt.show()
num_replace = {
'k' : 1000,
'm' : 1000000
}
def pure_number(s):
mult = 1.0
while s[-1] in num_replace:
mult *= num_replace[s[-1]]
s = s[:-1]
return round(float(s) * mult)
#Formatting fee as numeric
df=df[(df['Fee'] != 'Free transfer')]
df['Fee2']=df['Fee'].str.replace('Th.','k').str.replace('€','').str.replace('-','0')
df['Fee_Money']= [pure_number(x) for x in df['Fee2']]
fee_graph=df.groupby(by='Transfer_Season')['Fee_Money'].agg('mean').sort_values(ascending=True)
ax=fee_graph.plot.barh(figsize=(10,5), fontsize=11,color='grey')
ax.set_alpha(0.1)
ax.set_title("Transfer Fee & Season Avg Fee", fontsize=13)
ax.set_ylabel("Season", fontsize=13);
plt.show()
fig = px.pie(df, names='Main_position')
fig.show()
value=df.groupby(by='Main_position')['Fee_Money'].agg('mean').sort_values(ascending=True)
ax=value.plot.barh(figsize=(15,7), fontsize=13,color='darkblue')
ax.set_alpha(0.1)
ax.set_title("Transfer Fee & Position", fontsize=18)
ax.set_ylabel("Position", fontsize=15);
plt.show()
Let's explore the dataset a little bit. How many players exist in each position? Let's use team_position field first to find answers to our questions.
Some time difference features created in national stats and stats section have timedelta64 data type. In the below, i get the "days" from the data.
df['last_played_game_recency_National_30']=df['last_played_game_recency_National_30'].astype('timedelta64[D]')
df['last_played_game_recency_National_20']=df['last_played_game_recency_National_20'].astype('timedelta64[D]')
df['last_played_game_recency_National_10']=df['last_played_game_recency_National_10'].astype('timedelta64[D]')
df['last_played_game_recency_National_5']=df['last_played_game_recency_National_5'].astype('timedelta64[D]')
df['last_played_game_recency_30']=df['last_played_game_recency_30'].astype('timedelta64[D]')
df['last_played_game_recency_20']=df['last_played_game_recency_20'].astype('timedelta64[D]')
df['last_played_game_recency_10']=df['last_played_game_recency_10'].astype('timedelta64[D]')
df['last_played_game_recency_5']=df['last_played_game_recency_5'].astype('timedelta64[D]')
In the dataset, foot is specified as categorical variable. I am going to create dummy variables for foot which will give if the player is left footed, right footed or use both.
df['Foot'].value_counts()
right 6622 left 2363 both 512 Name: Foot, dtype: int64
foot_dummies=pd.get_dummies(df.Foot)
Main position of a football player is categorical, i will get dummy variables for each main position.
df['Main_position'].value_counts()
Centre-Forward 1892 Centre-Back 1506 Central Midfield 1083 Left Winger 827 Defensive Midfield 784 Right Winger 727 Attacking Midfield 686 Left-Back 626 Goalkeeper 522 Right-Back 518 Left Midfield 122 Right Midfield 107 Second Striker 97 Name: Main_position, dtype: int64
main_position_dummies=pd.get_dummies(df.Main_position)
In the profile dataset, positions footballer can play is specified as main position, other position1 and other position2. I created a new variable as number of positions by using those fields.Every football player has main position but, some football players can playin multiple positions.
sum(df['Main_position']=='')
0
sum(df['other_position1']=='')
2152
sum(df['other_position2']=='')
4463
df['NOF_position'] = [len( [a for a in [x,y,z] if a]) for x,y,z in zip(df['Main_position'],df['other_position1'],df['other_position2'])]
In each transfer, country left and country joined fields are available. I will define a threshold 50 and get dummy variable for each field if value count of country is greater than threshold.Below 50 is gathered to be "uncommon" field.
df['Country_Left'].unique()
array(['Germany', 'Mexico', 'China', 'Brazil', 'Italy', 'Argentina', nan,
'Slovenia', 'Colombia', 'Uruguay', 'Spain', 'Belgium',
'Korea, South', 'Norway', 'Sweden', 'France', 'Japan', 'Croatia',
'Ukraine', 'Czech Republic', 'Austria', 'Greece', 'Belarus',
'Kazakhstan', 'Australia', 'Portugal', 'Georgia', 'Thailand',
'Switzerland', 'Israel', 'Albania', 'Peru', 'Iceland', 'Lithuania',
'Netherlands', 'Romania', 'Poland', 'Russia', 'England', 'Egypt',
'Uzbekistan', 'Bulgaria', 'United States', 'Venezuela', 'Turkey',
'Algeria', 'Serbia', 'Cyprus', 'Morocco', 'Denmark', 'Costa Rica',
'Tunisia', 'Hungary', 'Slovakia', 'Montenegro', 'Finland',
'Bosnia-Herzegovina', 'Chile', 'Ecuador', 'South Africa',
'Scotland', 'United Arab Emirates', 'Iran', 'Moldova', 'Latvia',
'Saudi Arabia', 'Vietnam', 'Malta', 'Ghana', 'Azerbaijan',
'Luxembourg', 'Qatar', 'Lebanon', 'Malaysia', 'Paraguay',
'Hongkong', 'Estonia', 'India', 'North Macedonia', 'Nigeria',
'Ireland', 'Singapore', 'Armenia', 'Indonesia', 'Kosovo', 'Canada',
'Tajikistan'], dtype=object)
threshold = 50
counts = df.Country_Left.value_counts()
repl = counts[counts <= threshold].index
country_left_dummies=pd.get_dummies(df.Country_Left.replace(repl, 'uncommon'))
country_left_dummies=country_left_dummies.add_suffix('_c_left')
df['Country_Joined'].unique()
array(['Germany', 'Mexico', 'China', 'Switzerland', 'Spain', 'Brazil',
'Argentina', 'Denmark', 'Portugal', 'France', 'Netherlands',
'Belgium', nan, 'Japan', 'Czech Republic', 'Greece', 'Russia',
'Korea, South', 'Italy', 'Thailand', 'Romania', 'Peru', 'Norway',
'Belarus', 'Sweden', 'Poland', 'England', 'Egypt', 'Uzbekistan',
'Israel', 'United Arab Emirates', 'Turkey', 'Saudi Arabia',
'Algeria', 'Serbia', 'Austria', 'Bulgaria', 'Morocco',
'Costa Rica', 'Tunisia', 'Slovenia', 'United States', 'Scotland',
'Uruguay', 'Cyprus', 'Chile', 'Qatar', 'Lebanon', 'Croatia',
'Ukraine', 'Albania', 'Hungary', 'Iceland', 'Ecuador', 'Slovakia',
'Moldova', 'Colombia', 'Azerbaijan', 'Iran', 'Latvia',
'Bosnia-Herzegovina', 'Luxembourg', 'Malta', 'South Africa',
'Kazakhstan', 'Finland', 'Lithuania', 'Paraguay', 'Hongkong',
'Malaysia', 'Georgia', 'India', 'Indonesia', 'North Macedonia',
'Australia', 'Montenegro', 'Singapore', 'Venezuela', 'Tajikistan',
'Armenia', 'Guatemala', 'Bangladesh', 'Kosovo'], dtype=object)
threshold = 50
counts = df.Country_Joined.value_counts()
repl = counts[counts <= threshold].index
country_joined_dummies=pd.get_dummies(df.Country_Joined.replace(repl, 'uncommon'))
country_joined_dummies=country_joined_dummies.add_suffix('_c_joined')
In each transfer, fee of transfer should be highly correlated to the remaining contract time of footballers in the existing team. This value is given for some football players but format is need to be changed. I calculated remaining months of existing contract.
df['Contract_Left_at_Time_of_transfer']
0 02 Years 05 Months 29 Days (Jun 30, 2018)
1 NaN
2 01 Years 11 Months 29 Days (Dec 31, 2017)
3 11 Months 29 Days (Dec 31, 2016)
4 NaN
...
9494 NaN
9495 01 Years 08 Months 14 Days (Jun 30, 2022)
9496 NaN
9497 02 Years 07 Months 14 Days (May 31, 2023)
9498 08 Months 13 Days (Jun 30, 2021)
Name: Contract_Left_at_Time_of_transfer, Length: 9497, dtype: object
In each transfer, fee of transfer should be highly correlated to the remaining contract time of footballers in the existing team. This value is given for some football players but format is need to be changed. I calculated remaining months of existing contract.
aa=df['Contract_Left_at_Time_of_transfer'].str.split(' ',expand=True)
aa.columns = ['aa','bb','cc','dd','ee','ff','gg','hh','ii','jj']
aa.fillna(0,inplace=True)
df['months1']=[int(col1)*12 if col2 == 'Years' else 0 for col1,col2 in zip(aa['aa'],aa['bb'])]
df['months2']=[int(col1) if col2 == 'Months' else 0 for col1,col2 in zip(aa['aa'],aa['bb'])]
df['months3']=[int(col1) if col2 == 'Months' else 0 for col1,col2 in zip(aa['cc'],aa['dd'])]
df['remaining_contract_month']=df['months1']+df['months2']+df['months3']
df['Contract_Left_at_Time_of_transfer']=df['Contract_Left_at_Time_of_transfer'].fillna('Nan')
df['remaining_contract_month']= [x if y!= 'Nan' else np.nan for x,y in zip(df['remaining_contract_month'], df['Contract_Left_at_Time_of_transfer'])]
df.drop(['months1','months2','months3','Contract_Left_at_Time_of_transfer'],axis=1,inplace=True)
Age field should be formatted to use in the model.
aa=df['Age_at_Time_of_transfer'].str.split(' ',expand=True)
aa.columns = ['aa','bb','cc','dd','ee','ff']
df['months1']=[int(col1)*12 if col2 == 'years' else 0 for col1,col2 in zip(aa['aa'],aa['bb'])]
df['months2']=[int(col1) if col2 == 'months' else 0 for col1,col2 in zip(aa['aa'],aa['bb'])]
df['months3']=[int(col1) if col2 == 'months' else 0 for col1,col2 in zip(aa['cc'],aa['dd'])]
df['Age_month']=df['months1']+df['months2']+df['months3']
df['Age_at_Time_of_transfer']=df['Age_at_Time_of_transfer'].fillna('Nan')
df['Age_month']= [x if y!= 'Nan' else np.nan for x,y in zip(df['Age_month'], df['Age_at_Time_of_transfer'])]
df.drop(['months1','months2','months3','Age_at_Time_of_transfer'],axis=1,inplace=True)
Height field should be changed and formatted to be float.
df['Height'] = [float(x.replace('m','').replace(',','.'))*100 for x in df['Height'] ]
Market value at the time of the transfer should be formatted
df['Market_Value_at_Time_of_transfer2']=df['Market_Value_at_Time_of_transfer'].str.replace('Th.','k').str.replace('€','').str.replace('-','0')
df['Market_Value_Money']= [pure_number(x) for x in df['Market_Value_at_Time_of_transfer2']]
Fee is the target variable, should be float
df['Fee']
0 €8.00m
1 €7.30m
2 €7.05m
3 €6.25m
4 €5.50m
...
9494 €278Th.
9495 €100Th.
9496 €35Th.
9497 €186Th.
9498 €35Th.
Name: Fee, Length: 9497, dtype: object
df['Fee2']=df['Fee'].str.replace('Th.','k').str.replace('€','').str.replace('-','0')
df['Fee_Money']= [pure_number(x) for x in df['Fee2']]
Some columns are removed not to use in prediction.
df=df.merge(country_joined_dummies,left_index=True,right_index=True)
df=df.merge(country_left_dummies,left_index=True,right_index=True)
df=df.merge(main_position_dummies,left_index=True,right_index=True)
df=df.merge(foot_dummies,left_index=True,right_index=True)
df.drop(['Transfer_Link_x','Country_Left','Country_Joined','Foot','Main_position',
'Transfer_id',
'id_x',
'Avg_Market_Value',
'Tot_Fee_Loan_Fee_Inc',
'Market_Value_at_Time_of_transfer',
'Market_Value_at_Time_of_transfer2',
'Team_Left_Tier',
'Team_Joined_Tier',
'Team_Left_League',
'Team_Joined_League',
'Fee',
'Transfer_Season',
'Transfer_Date_x',
'Team_Left',
'Team_Joined',
'Fee2',
'Transfer_Link_y',
'id_y',
'Transfer_Date_y',
'Date_of_birth',
'Place_of_birth',
'Position','other_position1',
'other_position2','Citizenship'],axis=1,inplace=True)
Let's try to predict the value of the players using the features. I will use LightGBM and Keras for prediction. After i will compare the results of these 2 algorithms
LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:
Faster training speed and higher efficiency.
Lower memory usage.
Better accuracy.
Support of parallel and GPU learning.
Capable of handling large-scale data.
Most decision tree learning algorithms grow trees by level (depth)-wise, like the following image:

LightGBM grows trees leaf-wise. It will choose the leaf with max delta loss to grow. Holding #leaf fixed, leaf-wise algorithms tend to achieve lower loss than level-wise algorithms.
Leaf-wise may cause over-fitting when data is small, so LightGBM includes the max_depth parameter to limit tree depth. However, trees still grow leaf-wise even when max_depth is specified.

Source : LightGBM
# Define X ,y values
y=df['Fee_Money']
X=df.drop(['Fee_Money'],axis=1)
# Create training and test sets
x_train2, x_test2, y_train2, y_test2 = train_test_split(X, y, test_size=0.3, random_state=1907)
# LGBM wants this
x_train=x_train2.values
x_test=x_test2.values
y_train=y_train2.values
y_test=y_test2.values
#Instainsiate the model
model = LGBMRegressor(boosting_type='gbdt', objective='regression', metric='mae',bagging_fraction=0.8,
feature_fraction=0.8, reg_lambda=0.9,n_estimators=400, importance_type='split')
#fit the model
model.fit(x_train, y_train, eval_set= [(x_train, y_train), (x_test, y_test)],early_stopping_rounds=50)
[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] training's l1: 4.09265e+06 valid_1's l1: 3.87046e+06 Training until validation scores don't improve for 50 rounds [2] training's l1: 3.7606e+06 valid_1's l1: 3.56852e+06 [3] training's l1: 3.46555e+06 valid_1's l1: 3.30301e+06 [4] training's l1: 3.20847e+06 valid_1's l1: 3.06779e+06 [5] training's l1: 2.98063e+06 valid_1's l1: 2.8648e+06 [6] training's l1: 2.85553e+06 valid_1's l1: 2.76279e+06 [7] training's l1: 2.74258e+06 valid_1's l1: 2.67871e+06 [8] training's l1: 2.56817e+06 valid_1's l1: 2.52998e+06 [9] training's l1: 2.41507e+06 valid_1's l1: 2.40292e+06 [10] training's l1: 2.27937e+06 valid_1's l1: 2.29264e+06 [11] training's l1: 2.15677e+06 valid_1's l1: 2.1944e+06 [12] training's l1: 2.08419e+06 valid_1's l1: 2.14926e+06 [13] training's l1: 1.98695e+06 valid_1's l1: 2.07216e+06 [14] training's l1: 1.93416e+06 valid_1's l1: 2.03713e+06 [15] training's l1: 1.85531e+06 valid_1's l1: 1.97767e+06 [16] training's l1: 1.78167e+06 valid_1's l1: 1.92653e+06 [17] training's l1: 1.71817e+06 valid_1's l1: 1.8813e+06 [18] training's l1: 1.65884e+06 valid_1's l1: 1.8371e+06 [19] training's l1: 1.60973e+06 valid_1's l1: 1.79843e+06 [20] training's l1: 1.56753e+06 valid_1's l1: 1.76687e+06 [21] training's l1: 1.52528e+06 valid_1's l1: 1.7405e+06 [22] training's l1: 1.4878e+06 valid_1's l1: 1.71286e+06 [23] training's l1: 1.45308e+06 valid_1's l1: 1.69197e+06 [24] training's l1: 1.4228e+06 valid_1's l1: 1.67771e+06 [25] training's l1: 1.39525e+06 valid_1's l1: 1.6631e+06 [26] training's l1: 1.3705e+06 valid_1's l1: 1.64841e+06 [27] training's l1: 1.34856e+06 valid_1's l1: 1.63778e+06 [28] training's l1: 1.32594e+06 valid_1's l1: 1.62155e+06 [29] training's l1: 1.30551e+06 valid_1's l1: 1.61245e+06 [30] training's l1: 1.28489e+06 valid_1's l1: 1.60287e+06 [31] training's l1: 1.2687e+06 valid_1's l1: 1.59332e+06 [32] training's l1: 1.25253e+06 valid_1's l1: 1.58963e+06 [33] training's l1: 1.23648e+06 valid_1's l1: 1.58648e+06 [34] training's l1: 1.22151e+06 valid_1's l1: 1.58284e+06 [35] training's l1: 1.20966e+06 valid_1's l1: 1.57955e+06 [36] training's l1: 1.19568e+06 valid_1's l1: 1.57586e+06 [37] training's l1: 1.18286e+06 valid_1's l1: 1.56937e+06 [38] training's l1: 1.17238e+06 valid_1's l1: 1.56728e+06 [39] training's l1: 1.15986e+06 valid_1's l1: 1.56503e+06 [40] training's l1: 1.14846e+06 valid_1's l1: 1.55852e+06 [41] training's l1: 1.13798e+06 valid_1's l1: 1.55699e+06 [42] training's l1: 1.12858e+06 valid_1's l1: 1.55546e+06 [43] training's l1: 1.11902e+06 valid_1's l1: 1.55295e+06 [44] training's l1: 1.11146e+06 valid_1's l1: 1.55273e+06 [45] training's l1: 1.10217e+06 valid_1's l1: 1.55046e+06 [46] training's l1: 1.09474e+06 valid_1's l1: 1.55e+06 [47] training's l1: 1.08697e+06 valid_1's l1: 1.54738e+06 [48] training's l1: 1.07912e+06 valid_1's l1: 1.54724e+06 [49] training's l1: 1.07346e+06 valid_1's l1: 1.54641e+06 [50] training's l1: 1.06683e+06 valid_1's l1: 1.54413e+06 [51] training's l1: 1.05901e+06 valid_1's l1: 1.54208e+06 [52] training's l1: 1.05237e+06 valid_1's l1: 1.54174e+06 [53] training's l1: 1.0466e+06 valid_1's l1: 1.54251e+06 [54] training's l1: 1.03934e+06 valid_1's l1: 1.54114e+06 [55] training's l1: 1.03251e+06 valid_1's l1: 1.53744e+06 [56] training's l1: 1.02595e+06 valid_1's l1: 1.54073e+06 [57] training's l1: 1.02098e+06 valid_1's l1: 1.54005e+06 [58] training's l1: 1.01631e+06 valid_1's l1: 1.54025e+06 [59] training's l1: 1.01082e+06 valid_1's l1: 1.53988e+06 [60] training's l1: 1.00545e+06 valid_1's l1: 1.54041e+06 [61] training's l1: 999219 valid_1's l1: 1.54233e+06 [62] training's l1: 994663 valid_1's l1: 1.54357e+06 [63] training's l1: 988863 valid_1's l1: 1.54391e+06 [64] training's l1: 983400 valid_1's l1: 1.54294e+06 [65] training's l1: 978609 valid_1's l1: 1.54279e+06 [66] training's l1: 972493 valid_1's l1: 1.54256e+06 [67] training's l1: 968064 valid_1's l1: 1.5433e+06 [68] training's l1: 963471 valid_1's l1: 1.54376e+06 [69] training's l1: 957988 valid_1's l1: 1.54515e+06 [70] training's l1: 954486 valid_1's l1: 1.54563e+06 [71] training's l1: 950138 valid_1's l1: 1.54615e+06 [72] training's l1: 944728 valid_1's l1: 1.54676e+06 [73] training's l1: 939990 valid_1's l1: 1.54707e+06 [74] training's l1: 934899 valid_1's l1: 1.54719e+06 [75] training's l1: 930746 valid_1's l1: 1.54744e+06 [76] training's l1: 927513 valid_1's l1: 1.54795e+06 [77] training's l1: 923624 valid_1's l1: 1.54815e+06 [78] training's l1: 919157 valid_1's l1: 1.54791e+06 [79] training's l1: 916241 valid_1's l1: 1.54725e+06 [80] training's l1: 911020 valid_1's l1: 1.54706e+06 [81] training's l1: 905939 valid_1's l1: 1.54664e+06 [82] training's l1: 901920 valid_1's l1: 1.54784e+06 [83] training's l1: 898171 valid_1's l1: 1.54858e+06 [84] training's l1: 895049 valid_1's l1: 1.55025e+06 [85] training's l1: 891216 valid_1's l1: 1.55166e+06 [86] training's l1: 887022 valid_1's l1: 1.55144e+06 [87] training's l1: 882286 valid_1's l1: 1.55046e+06 [88] training's l1: 879978 valid_1's l1: 1.55191e+06 [89] training's l1: 876304 valid_1's l1: 1.55214e+06 [90] training's l1: 873016 valid_1's l1: 1.55417e+06 [91] training's l1: 869207 valid_1's l1: 1.55418e+06 [92] training's l1: 865129 valid_1's l1: 1.55425e+06 [93] training's l1: 861907 valid_1's l1: 1.55552e+06 [94] training's l1: 858334 valid_1's l1: 1.55645e+06 [95] training's l1: 856034 valid_1's l1: 1.55706e+06 [96] training's l1: 852584 valid_1's l1: 1.55974e+06 [97] training's l1: 849070 valid_1's l1: 1.5602e+06 [98] training's l1: 845514 valid_1's l1: 1.56083e+06 [99] training's l1: 841920 valid_1's l1: 1.56151e+06 [100] training's l1: 838767 valid_1's l1: 1.56121e+06 [101] training's l1: 835467 valid_1's l1: 1.56228e+06 [102] training's l1: 832438 valid_1's l1: 1.56194e+06 [103] training's l1: 828735 valid_1's l1: 1.56172e+06 [104] training's l1: 824175 valid_1's l1: 1.5616e+06 [105] training's l1: 821446 valid_1's l1: 1.56244e+06 Early stopping, best iteration is: [55] training's l1: 1.03251e+06 valid_1's l1: 1.53744e+06
LGBMRegressor(bagging_fraction=0.8, feature_fraction=0.8, metric='mae',
n_estimators=400, objective='regression', reg_lambda=0.9)
# Hyper paramter optimization : Grid Search
parameters = {'n_estimators': [400,800,1200],
'learning_rate': [0.1, 0.2],
'max_depth':[-1,5,6],
'num_leaves' : [100,200]
}
gridSearchCV = GridSearchCV(estimator = model,
param_grid = parameters,
scoring='neg_mean_absolute_error',
n_jobs=1,
iid=False,
verbose=1,
cv=3)
gridSearchCV.fit(x_train,y_train,eval_set = (x_test, y_test),early_stopping_rounds=30)
Fitting 3 folds for each of 36 candidates, totalling 108 fits
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[1] valid_0's l1: 3.8804e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.58147e+06 [3] valid_0's l1: 3.31561e+06 [4] valid_0's l1: 3.08939e+06 [5] valid_0's l1: 2.89077e+06 [6] valid_0's l1: 2.79162e+06 [7] valid_0's l1: 2.70318e+06 [8] valid_0's l1: 2.55683e+06 [9] valid_0's l1: 2.42573e+06 [10] valid_0's l1: 2.31887e+06 [11] valid_0's l1: 2.22359e+06 [12] valid_0's l1: 2.18024e+06 [13] valid_0's l1: 2.09433e+06 [14] valid_0's l1: 2.06667e+06 [15] valid_0's l1: 2.0008e+06 [16] valid_0's l1: 1.94363e+06 [17] valid_0's l1: 1.89901e+06 [18] valid_0's l1: 1.85718e+06 [19] valid_0's l1: 1.81714e+06 [20] valid_0's l1: 1.78493e+06 [21] valid_0's l1: 1.75613e+06 [22] valid_0's l1: 1.73142e+06 [23] valid_0's l1: 1.71209e+06 [24] valid_0's l1: 1.69322e+06 [25] valid_0's l1: 1.67855e+06 [26] valid_0's l1: 1.66736e+06 [27] valid_0's l1: 1.66042e+06 [28] valid_0's l1: 1.64903e+06 [29] valid_0's l1: 1.64043e+06 [30] valid_0's l1: 1.63364e+06 [31] valid_0's l1: 1.62925e+06 [32] valid_0's l1: 1.62312e+06 [33] valid_0's l1: 1.62148e+06 [34] valid_0's l1: 1.6214e+06 [35] valid_0's l1: 1.61554e+06 [36] valid_0's l1: 1.61147e+06 [37] valid_0's l1: 1.61104e+06 [38] valid_0's l1: 1.60897e+06 [39] valid_0's l1: 1.61109e+06 [40] valid_0's l1: 1.60755e+06 [41] valid_0's l1: 1.60865e+06 [42] valid_0's l1: 1.61138e+06 [43] valid_0's l1: 1.61081e+06 [44] valid_0's l1: 1.61109e+06 [45] valid_0's l1: 1.61105e+06 [46] valid_0's l1: 1.6129e+06 [47] valid_0's l1: 1.61142e+06 [48] valid_0's l1: 1.61223e+06 [49] valid_0's l1: 1.61085e+06 [50] valid_0's l1: 1.6142e+06 [51] valid_0's l1: 1.61623e+06 [52] valid_0's l1: 1.61796e+06 [53] valid_0's l1: 1.6188e+06 [54] valid_0's l1: 1.62176e+06 [55] valid_0's l1: 1.62242e+06 [56] valid_0's l1: 1.62088e+06 [57] valid_0's l1: 1.62222e+06 [58] valid_0's l1: 1.62347e+06 [59] valid_0's l1: 1.62433e+06 [60] valid_0's l1: 1.62428e+06 [61] valid_0's l1: 1.6274e+06 [62] valid_0's l1: 1.62864e+06 [63] valid_0's l1: 1.62993e+06 [64] valid_0's l1: 1.62965e+06 [65] valid_0's l1: 1.63218e+06 [66] valid_0's l1: 1.63269e+06 [67] valid_0's l1: 1.63347e+06 [68] valid_0's l1: 1.63408e+06 [69] valid_0's l1: 1.63432e+06 [70] valid_0's l1: 1.63412e+06 Early stopping, best iteration is: [40] valid_0's l1: 1.60755e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.82234e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.53738e+06 [3] valid_0's l1: 3.28278e+06 [4] valid_0's l1: 3.05055e+06 [5] valid_0's l1: 2.85395e+06 [6] valid_0's l1: 2.76027e+06 [7] valid_0's l1: 2.67207e+06 [8] valid_0's l1: 2.52479e+06 [9] valid_0's l1: 2.39693e+06 [10] valid_0's l1: 2.2844e+06 [11] valid_0's l1: 2.18771e+06 [12] valid_0's l1: 2.13956e+06 [13] valid_0's l1: 2.06551e+06 [14] valid_0's l1: 2.03037e+06 [15] valid_0's l1: 1.97266e+06 [16] valid_0's l1: 1.92277e+06 [17] valid_0's l1: 1.88071e+06 [18] valid_0's l1: 1.84012e+06 [19] valid_0's l1: 1.80914e+06 [20] valid_0's l1: 1.78341e+06 [21] valid_0's l1: 1.75977e+06 [22] valid_0's l1: 1.73938e+06 [23] valid_0's l1: 1.71884e+06 [24] valid_0's l1: 1.704e+06 [25] valid_0's l1: 1.68982e+06 [26] valid_0's l1: 1.67853e+06 [27] valid_0's l1: 1.66707e+06 [28] valid_0's l1: 1.6575e+06 [29] valid_0's l1: 1.64879e+06 [30] valid_0's l1: 1.64292e+06 [31] valid_0's l1: 1.63945e+06 [32] valid_0's l1: 1.6373e+06 [33] valid_0's l1: 1.63527e+06 [34] valid_0's l1: 1.63174e+06 [35] valid_0's l1: 1.6321e+06 [36] valid_0's l1: 1.62921e+06 [37] valid_0's l1: 1.63137e+06 [38] valid_0's l1: 1.62857e+06 [39] valid_0's l1: 1.62869e+06 [40] valid_0's l1: 1.62636e+06 [41] valid_0's l1: 1.62635e+06 [42] valid_0's l1: 1.62345e+06 [43] valid_0's l1: 1.62422e+06 [44] valid_0's l1: 1.62453e+06 [45] valid_0's l1: 1.62611e+06 [46] valid_0's l1: 1.6248e+06 [47] valid_0's l1: 1.62704e+06 [48] valid_0's l1: 1.62781e+06 [49] valid_0's l1: 1.6272e+06 [50] valid_0's l1: 1.62614e+06 [51] valid_0's l1: 1.62683e+06 [52] valid_0's l1: 1.62589e+06 [53] valid_0's l1: 1.62841e+06 [54] valid_0's l1: 1.62995e+06 [55] valid_0's l1: 1.63114e+06 [56] valid_0's l1: 1.63104e+06 [57] valid_0's l1: 1.63222e+06 [58] valid_0's l1: 1.63222e+06 [59] valid_0's l1: 1.63304e+06 [60] valid_0's l1: 1.63325e+06 [61] valid_0's l1: 1.63288e+06 [62] valid_0's l1: 1.63368e+06 [63] valid_0's l1: 1.634e+06 [64] valid_0's l1: 1.63577e+06 [65] valid_0's l1: 1.63388e+06 [66] valid_0's l1: 1.63492e+06 [67] valid_0's l1: 1.63456e+06 [68] valid_0's l1: 1.63418e+06 [69] valid_0's l1: 1.63338e+06 [70] valid_0's l1: 1.6334e+06 [71] valid_0's l1: 1.63182e+06 [72] valid_0's l1: 1.63426e+06 Early stopping, best iteration is: [42] valid_0's l1: 1.62345e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.90331e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.60346e+06 [3] valid_0's l1: 3.33147e+06 [4] valid_0's l1: 3.0943e+06 [5] valid_0's l1: 2.88691e+06 [6] valid_0's l1: 2.77737e+06 [7] valid_0's l1: 2.68585e+06 [8] valid_0's l1: 2.53803e+06 [9] valid_0's l1: 2.4083e+06 [10] valid_0's l1: 2.29773e+06 [11] valid_0's l1: 2.19713e+06 [12] valid_0's l1: 2.14466e+06 [13] valid_0's l1: 2.06369e+06 [14] valid_0's l1: 2.03015e+06 [15] valid_0's l1: 1.97551e+06 [16] valid_0's l1: 1.92629e+06 [17] valid_0's l1: 1.88563e+06 [18] valid_0's l1: 1.84773e+06 [19] valid_0's l1: 1.81126e+06 [20] valid_0's l1: 1.7812e+06 [21] valid_0's l1: 1.75382e+06 [22] valid_0's l1: 1.73287e+06 [23] valid_0's l1: 1.71452e+06 [24] valid_0's l1: 1.69565e+06 [25] valid_0's l1: 1.68183e+06 [26] valid_0's l1: 1.667e+06 [27] valid_0's l1: 1.6531e+06 [28] valid_0's l1: 1.64525e+06 [29] valid_0's l1: 1.63498e+06 [30] valid_0's l1: 1.62945e+06 [31] valid_0's l1: 1.62718e+06 [32] valid_0's l1: 1.61879e+06 [33] valid_0's l1: 1.61602e+06 [34] valid_0's l1: 1.6127e+06 [35] valid_0's l1: 1.60762e+06 [36] valid_0's l1: 1.60376e+06 [37] valid_0's l1: 1.60473e+06 [38] valid_0's l1: 1.60411e+06 [39] valid_0's l1: 1.6067e+06 [40] valid_0's l1: 1.60682e+06 [41] valid_0's l1: 1.60711e+06 [42] valid_0's l1: 1.60511e+06 [43] valid_0's l1: 1.60621e+06 [44] valid_0's l1: 1.60682e+06 [45] valid_0's l1: 1.60734e+06 [46] valid_0's l1: 1.60853e+06 [47] valid_0's l1: 1.61095e+06 [48] valid_0's l1: 1.61156e+06 [49] valid_0's l1: 1.61278e+06 [50] valid_0's l1: 1.61521e+06 [51] valid_0's l1: 1.61715e+06 [52] valid_0's l1: 1.61574e+06 [53] valid_0's l1: 1.61809e+06 [54] valid_0's l1: 1.61949e+06 [55] valid_0's l1: 1.61926e+06 [56] valid_0's l1: 1.61998e+06 [57] valid_0's l1: 1.62428e+06 [58] valid_0's l1: 1.62571e+06 [59] valid_0's l1: 1.62745e+06 [60] valid_0's l1: 1.6269e+06 [61] valid_0's l1: 1.62842e+06 [62] valid_0's l1: 1.62883e+06 [63] valid_0's l1: 1.62782e+06 [64] valid_0's l1: 1.62625e+06 [65] valid_0's l1: 1.62601e+06 [66] valid_0's l1: 1.6253e+06 Early stopping, best iteration is: [36] valid_0's l1: 1.60376e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.88063e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.58111e+06 [3] valid_0's l1: 3.31471e+06 [4] valid_0's l1: 3.08971e+06 [5] valid_0's l1: 2.89137e+06 [6] valid_0's l1: 2.79091e+06 [7] valid_0's l1: 2.7016e+06 [8] valid_0's l1: 2.5545e+06 [9] valid_0's l1: 2.42672e+06 [10] valid_0's l1: 2.31773e+06 [11] valid_0's l1: 2.22329e+06 [12] valid_0's l1: 2.17855e+06 [13] valid_0's l1: 2.09992e+06 [14] valid_0's l1: 2.07693e+06 [15] valid_0's l1: 2.00991e+06 [16] valid_0's l1: 1.95164e+06 [17] valid_0's l1: 1.90423e+06 [18] valid_0's l1: 1.86449e+06 [19] valid_0's l1: 1.82493e+06 [20] valid_0's l1: 1.79157e+06 [21] valid_0's l1: 1.75899e+06 [22] valid_0's l1: 1.7361e+06 [23] valid_0's l1: 1.71289e+06 [24] valid_0's l1: 1.69566e+06 [25] valid_0's l1: 1.68045e+06 [26] valid_0's l1: 1.66649e+06 [27] valid_0's l1: 1.65602e+06 [28] valid_0's l1: 1.65022e+06 [29] valid_0's l1: 1.64358e+06 [30] valid_0's l1: 1.63472e+06 [31] valid_0's l1: 1.63274e+06 [32] valid_0's l1: 1.62521e+06 [33] valid_0's l1: 1.6268e+06 [34] valid_0's l1: 1.62937e+06 [35] valid_0's l1: 1.62197e+06 [36] valid_0's l1: 1.62251e+06 [37] valid_0's l1: 1.61791e+06 [38] valid_0's l1: 1.6191e+06 [39] valid_0's l1: 1.62166e+06 [40] valid_0's l1: 1.61971e+06 [41] valid_0's l1: 1.62036e+06 [42] valid_0's l1: 1.62022e+06 [43] valid_0's l1: 1.62122e+06 [44] valid_0's l1: 1.62306e+06 [45] valid_0's l1: 1.62404e+06 [46] valid_0's l1: 1.62464e+06 [47] valid_0's l1: 1.62581e+06 [48] valid_0's l1: 1.6233e+06 [49] valid_0's l1: 1.62433e+06 [50] valid_0's l1: 1.62647e+06 [51] valid_0's l1: 1.62283e+06 [52] valid_0's l1: 1.62738e+06 [53] valid_0's l1: 1.62974e+06 [54] valid_0's l1: 1.63427e+06 [55] valid_0's l1: 1.63703e+06 [56] valid_0's l1: 1.63911e+06 [57] valid_0's l1: 1.6396e+06 [58] valid_0's l1: 1.63766e+06 [59] valid_0's l1: 1.64045e+06 [60] valid_0's l1: 1.64282e+06 [61] valid_0's l1: 1.64267e+06 [62] valid_0's l1: 1.64467e+06 [63] valid_0's l1: 1.64473e+06 [64] valid_0's l1: 1.64545e+06 [65] valid_0's l1: 1.64774e+06 [66] valid_0's l1: 1.64728e+06 [67] valid_0's l1: 1.64694e+06 Early stopping, best iteration is: [37] valid_0's l1: 1.61791e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.82314e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.53885e+06 [3] valid_0's l1: 3.28346e+06 [4] valid_0's l1: 3.05156e+06 [5] valid_0's l1: 2.85542e+06 [6] valid_0's l1: 2.75925e+06 [7] valid_0's l1: 2.66959e+06 [8] valid_0's l1: 2.52656e+06 [9] valid_0's l1: 2.40248e+06 [10] valid_0's l1: 2.29316e+06 [11] valid_0's l1: 2.1974e+06 [12] valid_0's l1: 2.15027e+06 [13] valid_0's l1: 2.07389e+06 [14] valid_0's l1: 2.04006e+06 [15] valid_0's l1: 1.98279e+06 [16] valid_0's l1: 1.93007e+06 [17] valid_0's l1: 1.88619e+06 [18] valid_0's l1: 1.84794e+06 [19] valid_0's l1: 1.8186e+06 [20] valid_0's l1: 1.78666e+06 [21] valid_0's l1: 1.76808e+06 [22] valid_0's l1: 1.75232e+06 [23] valid_0's l1: 1.73378e+06 [24] valid_0's l1: 1.71863e+06 [25] valid_0's l1: 1.70628e+06 [26] valid_0's l1: 1.69601e+06 [27] valid_0's l1: 1.68197e+06 [28] valid_0's l1: 1.67391e+06 [29] valid_0's l1: 1.66586e+06 [30] valid_0's l1: 1.66154e+06 [31] valid_0's l1: 1.65638e+06 [32] valid_0's l1: 1.65255e+06 [33] valid_0's l1: 1.65111e+06 [34] valid_0's l1: 1.65118e+06 [35] valid_0's l1: 1.64782e+06 [36] valid_0's l1: 1.64348e+06 [37] valid_0's l1: 1.64234e+06 [38] valid_0's l1: 1.6437e+06 [39] valid_0's l1: 1.64265e+06 [40] valid_0's l1: 1.64379e+06 [41] valid_0's l1: 1.64313e+06 [42] valid_0's l1: 1.6418e+06 [43] valid_0's l1: 1.64361e+06 [44] valid_0's l1: 1.64218e+06 [45] valid_0's l1: 1.64402e+06 [46] valid_0's l1: 1.64146e+06 [47] valid_0's l1: 1.64262e+06 [48] valid_0's l1: 1.64187e+06 [49] valid_0's l1: 1.64002e+06 [50] valid_0's l1: 1.63854e+06 [51] valid_0's l1: 1.63858e+06 [52] valid_0's l1: 1.63825e+06 [53] valid_0's l1: 1.64072e+06 [54] valid_0's l1: 1.64255e+06 [55] valid_0's l1: 1.64161e+06 [56] valid_0's l1: 1.64266e+06 [57] valid_0's l1: 1.64504e+06 [58] valid_0's l1: 1.64487e+06 [59] valid_0's l1: 1.64461e+06 [60] valid_0's l1: 1.6457e+06 [61] valid_0's l1: 1.64668e+06 [62] valid_0's l1: 1.64771e+06 [63] valid_0's l1: 1.64831e+06 [64] valid_0's l1: 1.64711e+06 [65] valid_0's l1: 1.64739e+06 [66] valid_0's l1: 1.6483e+06 [67] valid_0's l1: 1.64816e+06 [68] valid_0's l1: 1.64978e+06 [69] valid_0's l1: 1.65005e+06 [70] valid_0's l1: 1.65012e+06 [71] valid_0's l1: 1.65122e+06 [72] valid_0's l1: 1.6506e+06 [73] valid_0's l1: 1.6509e+06 [74] valid_0's l1: 1.6501e+06 [75] valid_0's l1: 1.65227e+06 [76] valid_0's l1: 1.65293e+06 [77] valid_0's l1: 1.65491e+06 [78] valid_0's l1: 1.65499e+06 [79] valid_0's l1: 1.65567e+06 [80] valid_0's l1: 1.65697e+06 [81] valid_0's l1: 1.65648e+06 [82] valid_0's l1: 1.65633e+06 Early stopping, best iteration is: [52] valid_0's l1: 1.63825e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.90347e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.60447e+06 [3] valid_0's l1: 3.33163e+06 [4] valid_0's l1: 3.09336e+06 [5] valid_0's l1: 2.88614e+06 [6] valid_0's l1: 2.77812e+06 [7] valid_0's l1: 2.68595e+06 [8] valid_0's l1: 2.53776e+06 [9] valid_0's l1: 2.40858e+06 [10] valid_0's l1: 2.29804e+06 [11] valid_0's l1: 2.20018e+06 [12] valid_0's l1: 2.14893e+06 [13] valid_0's l1: 2.07158e+06 [14] valid_0's l1: 2.03795e+06 [15] valid_0's l1: 1.98575e+06 [16] valid_0's l1: 1.93925e+06 [17] valid_0's l1: 1.89833e+06 [18] valid_0's l1: 1.85799e+06 [19] valid_0's l1: 1.81604e+06 [20] valid_0's l1: 1.78718e+06 [21] valid_0's l1: 1.76339e+06 [22] valid_0's l1: 1.73882e+06 [23] valid_0's l1: 1.71904e+06 [24] valid_0's l1: 1.70315e+06 [25] valid_0's l1: 1.68543e+06 [26] valid_0's l1: 1.67107e+06 [27] valid_0's l1: 1.66136e+06 [28] valid_0's l1: 1.65118e+06 [29] valid_0's l1: 1.64208e+06 [30] valid_0's l1: 1.63366e+06 [31] valid_0's l1: 1.63059e+06 [32] valid_0's l1: 1.62535e+06 [33] valid_0's l1: 1.62527e+06 [34] valid_0's l1: 1.62184e+06 [35] valid_0's l1: 1.62203e+06 [36] valid_0's l1: 1.62009e+06 [37] valid_0's l1: 1.61825e+06 [38] valid_0's l1: 1.61759e+06 [39] valid_0's l1: 1.61438e+06 [40] valid_0's l1: 1.61596e+06 [41] valid_0's l1: 1.61631e+06 [42] valid_0's l1: 1.61543e+06 [43] valid_0's l1: 1.61522e+06 [44] valid_0's l1: 1.61456e+06 [45] valid_0's l1: 1.61175e+06 [46] valid_0's l1: 1.61233e+06 [47] valid_0's l1: 1.61393e+06 [48] valid_0's l1: 1.61366e+06 [49] valid_0's l1: 1.61534e+06 [50] valid_0's l1: 1.61645e+06 [51] valid_0's l1: 1.61485e+06 [52] valid_0's l1: 1.61601e+06 [53] valid_0's l1: 1.61818e+06 [54] valid_0's l1: 1.61939e+06 [55] valid_0's l1: 1.61976e+06 [56] valid_0's l1: 1.61974e+06 [57] valid_0's l1: 1.62078e+06 [58] valid_0's l1: 1.62052e+06 [59] valid_0's l1: 1.62096e+06 [60] valid_0's l1: 1.61909e+06 [61] valid_0's l1: 1.62111e+06 [62] valid_0's l1: 1.62204e+06 [63] valid_0's l1: 1.624e+06 [64] valid_0's l1: 1.62425e+06 [65] valid_0's l1: 1.62319e+06 [66] valid_0's l1: 1.6254e+06 [67] valid_0's l1: 1.62587e+06 [68] valid_0's l1: 1.6266e+06 [69] valid_0's l1: 1.62549e+06 [70] valid_0's l1: 1.62726e+06 [71] valid_0's l1: 1.62715e+06 [72] valid_0's l1: 1.62843e+06 [73] valid_0's l1: 1.631e+06 [74] valid_0's l1: 1.62983e+06 [75] valid_0's l1: 1.63112e+06 Early stopping, best iteration is: [45] valid_0's l1: 1.61175e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.8804e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.58147e+06 [3] valid_0's l1: 3.31561e+06 [4] valid_0's l1: 3.08939e+06 [5] valid_0's l1: 2.89077e+06 [6] valid_0's l1: 2.79162e+06 [7] valid_0's l1: 2.70318e+06 [8] valid_0's l1: 2.55683e+06 [9] valid_0's l1: 2.42573e+06 [10] valid_0's l1: 2.31887e+06 [11] valid_0's l1: 2.22359e+06 [12] valid_0's l1: 2.18024e+06 [13] valid_0's l1: 2.09433e+06 [14] valid_0's l1: 2.06667e+06 [15] valid_0's l1: 2.0008e+06 [16] valid_0's l1: 1.94363e+06 [17] valid_0's l1: 1.89901e+06 [18] valid_0's l1: 1.85718e+06 [19] valid_0's l1: 1.81714e+06 [20] valid_0's l1: 1.78493e+06 [21] valid_0's l1: 1.75613e+06 [22] valid_0's l1: 1.73142e+06 [23] valid_0's l1: 1.71209e+06 [24] valid_0's l1: 1.69322e+06 [25] valid_0's l1: 1.67855e+06 [26] valid_0's l1: 1.66736e+06 [27] valid_0's l1: 1.66042e+06 [28] valid_0's l1: 1.64903e+06 [29] valid_0's l1: 1.64043e+06 [30] valid_0's l1: 1.63364e+06 [31] valid_0's l1: 1.62925e+06 [32] valid_0's l1: 1.62312e+06 [33] valid_0's l1: 1.62148e+06 [34] valid_0's l1: 1.6214e+06 [35] valid_0's l1: 1.61554e+06 [36] valid_0's l1: 1.61147e+06 [37] valid_0's l1: 1.61104e+06 [38] valid_0's l1: 1.60897e+06 [39] valid_0's l1: 1.61109e+06 [40] valid_0's l1: 1.60755e+06 [41] valid_0's l1: 1.60865e+06 [42] valid_0's l1: 1.61138e+06 [43] valid_0's l1: 1.61081e+06 [44] valid_0's l1: 1.61109e+06 [45] valid_0's l1: 1.61105e+06 [46] valid_0's l1: 1.6129e+06 [47] valid_0's l1: 1.61142e+06 [48] valid_0's l1: 1.61223e+06 [49] valid_0's l1: 1.61085e+06 [50] valid_0's l1: 1.6142e+06 [51] valid_0's l1: 1.61623e+06 [52] valid_0's l1: 1.61796e+06 [53] valid_0's l1: 1.6188e+06 [54] valid_0's l1: 1.62176e+06 [55] valid_0's l1: 1.62242e+06 [56] valid_0's l1: 1.62088e+06 [57] valid_0's l1: 1.62222e+06 [58] valid_0's l1: 1.62347e+06 [59] valid_0's l1: 1.62433e+06 [60] valid_0's l1: 1.62428e+06 [61] valid_0's l1: 1.6274e+06 [62] valid_0's l1: 1.62864e+06 [63] valid_0's l1: 1.62993e+06 [64] valid_0's l1: 1.62965e+06 [65] valid_0's l1: 1.63218e+06 [66] valid_0's l1: 1.63269e+06 [67] valid_0's l1: 1.63347e+06 [68] valid_0's l1: 1.63408e+06 [69] valid_0's l1: 1.63432e+06 [70] valid_0's l1: 1.63412e+06 Early stopping, best iteration is: [40] valid_0's l1: 1.60755e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.82234e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.53738e+06 [3] valid_0's l1: 3.28278e+06 [4] valid_0's l1: 3.05055e+06 [5] valid_0's l1: 2.85395e+06 [6] valid_0's l1: 2.76027e+06 [7] valid_0's l1: 2.67207e+06 [8] valid_0's l1: 2.52479e+06 [9] valid_0's l1: 2.39693e+06 [10] valid_0's l1: 2.2844e+06 [11] valid_0's l1: 2.18771e+06 [12] valid_0's l1: 2.13956e+06 [13] valid_0's l1: 2.06551e+06 [14] valid_0's l1: 2.03037e+06 [15] valid_0's l1: 1.97266e+06 [16] valid_0's l1: 1.92277e+06 [17] valid_0's l1: 1.88071e+06 [18] valid_0's l1: 1.84012e+06 [19] valid_0's l1: 1.80914e+06 [20] valid_0's l1: 1.78341e+06 [21] valid_0's l1: 1.75977e+06 [22] valid_0's l1: 1.73938e+06 [23] valid_0's l1: 1.71884e+06 [24] valid_0's l1: 1.704e+06 [25] valid_0's l1: 1.68982e+06 [26] valid_0's l1: 1.67853e+06 [27] valid_0's l1: 1.66707e+06 [28] valid_0's l1: 1.6575e+06 [29] valid_0's l1: 1.64879e+06 [30] valid_0's l1: 1.64292e+06 [31] valid_0's l1: 1.63945e+06 [32] valid_0's l1: 1.6373e+06 [33] valid_0's l1: 1.63527e+06 [34] valid_0's l1: 1.63174e+06 [35] valid_0's l1: 1.6321e+06 [36] valid_0's l1: 1.62921e+06 [37] valid_0's l1: 1.63137e+06 [38] valid_0's l1: 1.62857e+06 [39] valid_0's l1: 1.62869e+06 [40] valid_0's l1: 1.62636e+06 [41] valid_0's l1: 1.62635e+06 [42] valid_0's l1: 1.62345e+06 [43] valid_0's l1: 1.62422e+06 [44] valid_0's l1: 1.62453e+06 [45] valid_0's l1: 1.62611e+06 [46] valid_0's l1: 1.6248e+06 [47] valid_0's l1: 1.62704e+06 [48] valid_0's l1: 1.62781e+06 [49] valid_0's l1: 1.6272e+06 [50] valid_0's l1: 1.62614e+06 [51] valid_0's l1: 1.62683e+06 [52] valid_0's l1: 1.62589e+06 [53] valid_0's l1: 1.62841e+06 [54] valid_0's l1: 1.62995e+06 [55] valid_0's l1: 1.63114e+06 [56] valid_0's l1: 1.63104e+06 [57] valid_0's l1: 1.63222e+06 [58] valid_0's l1: 1.63222e+06 [59] valid_0's l1: 1.63304e+06 [60] valid_0's l1: 1.63325e+06 [61] valid_0's l1: 1.63288e+06 [62] valid_0's l1: 1.63368e+06 [63] valid_0's l1: 1.634e+06 [64] valid_0's l1: 1.63577e+06 [65] valid_0's l1: 1.63388e+06 [66] valid_0's l1: 1.63492e+06 [67] valid_0's l1: 1.63456e+06 [68] valid_0's l1: 1.63418e+06 [69] valid_0's l1: 1.63338e+06 [70] valid_0's l1: 1.6334e+06 [71] valid_0's l1: 1.63182e+06 [72] valid_0's l1: 1.63426e+06 Early stopping, best iteration is: [42] valid_0's l1: 1.62345e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.90331e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.60346e+06 [3] valid_0's l1: 3.33147e+06 [4] valid_0's l1: 3.0943e+06 [5] valid_0's l1: 2.88691e+06 [6] valid_0's l1: 2.77737e+06 [7] valid_0's l1: 2.68585e+06 [8] valid_0's l1: 2.53803e+06 [9] valid_0's l1: 2.4083e+06 [10] valid_0's l1: 2.29773e+06 [11] valid_0's l1: 2.19713e+06 [12] valid_0's l1: 2.14466e+06 [13] valid_0's l1: 2.06369e+06 [14] valid_0's l1: 2.03015e+06 [15] valid_0's l1: 1.97551e+06 [16] valid_0's l1: 1.92629e+06 [17] valid_0's l1: 1.88563e+06 [18] valid_0's l1: 1.84773e+06 [19] valid_0's l1: 1.81126e+06 [20] valid_0's l1: 1.7812e+06 [21] valid_0's l1: 1.75382e+06 [22] valid_0's l1: 1.73287e+06 [23] valid_0's l1: 1.71452e+06 [24] valid_0's l1: 1.69565e+06 [25] valid_0's l1: 1.68183e+06 [26] valid_0's l1: 1.667e+06 [27] valid_0's l1: 1.6531e+06 [28] valid_0's l1: 1.64525e+06 [29] valid_0's l1: 1.63498e+06 [30] valid_0's l1: 1.62945e+06 [31] valid_0's l1: 1.62718e+06 [32] valid_0's l1: 1.61879e+06 [33] valid_0's l1: 1.61602e+06 [34] valid_0's l1: 1.6127e+06 [35] valid_0's l1: 1.60762e+06 [36] valid_0's l1: 1.60376e+06 [37] valid_0's l1: 1.60473e+06 [38] valid_0's l1: 1.60411e+06 [39] valid_0's l1: 1.6067e+06 [40] valid_0's l1: 1.60682e+06 [41] valid_0's l1: 1.60711e+06 [42] valid_0's l1: 1.60511e+06 [43] valid_0's l1: 1.60621e+06 [44] valid_0's l1: 1.60682e+06 [45] valid_0's l1: 1.60734e+06 [46] valid_0's l1: 1.60853e+06 [47] valid_0's l1: 1.61095e+06 [48] valid_0's l1: 1.61156e+06 [49] valid_0's l1: 1.61278e+06 [50] valid_0's l1: 1.61521e+06 [51] valid_0's l1: 1.61715e+06 [52] valid_0's l1: 1.61574e+06 [53] valid_0's l1: 1.61809e+06 [54] valid_0's l1: 1.61949e+06 [55] valid_0's l1: 1.61926e+06 [56] valid_0's l1: 1.61998e+06 [57] valid_0's l1: 1.62428e+06 [58] valid_0's l1: 1.62571e+06 [59] valid_0's l1: 1.62745e+06 [60] valid_0's l1: 1.6269e+06 [61] valid_0's l1: 1.62842e+06 [62] valid_0's l1: 1.62883e+06 [63] valid_0's l1: 1.62782e+06 [64] valid_0's l1: 1.62625e+06 [65] valid_0's l1: 1.62601e+06 [66] valid_0's l1: 1.6253e+06 Early stopping, best iteration is: [36] valid_0's l1: 1.60376e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.88063e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.58111e+06 [3] valid_0's l1: 3.31471e+06 [4] valid_0's l1: 3.08971e+06 [5] valid_0's l1: 2.89137e+06 [6] valid_0's l1: 2.79091e+06 [7] valid_0's l1: 2.7016e+06 [8] valid_0's l1: 2.5545e+06 [9] valid_0's l1: 2.42672e+06 [10] valid_0's l1: 2.31773e+06 [11] valid_0's l1: 2.22329e+06 [12] valid_0's l1: 2.17855e+06 [13] valid_0's l1: 2.09992e+06 [14] valid_0's l1: 2.07693e+06 [15] valid_0's l1: 2.00991e+06 [16] valid_0's l1: 1.95164e+06 [17] valid_0's l1: 1.90423e+06 [18] valid_0's l1: 1.86449e+06 [19] valid_0's l1: 1.82493e+06 [20] valid_0's l1: 1.79157e+06 [21] valid_0's l1: 1.75899e+06 [22] valid_0's l1: 1.7361e+06 [23] valid_0's l1: 1.71289e+06 [24] valid_0's l1: 1.69566e+06 [25] valid_0's l1: 1.68045e+06 [26] valid_0's l1: 1.66649e+06 [27] valid_0's l1: 1.65602e+06 [28] valid_0's l1: 1.65022e+06 [29] valid_0's l1: 1.64358e+06 [30] valid_0's l1: 1.63472e+06 [31] valid_0's l1: 1.63274e+06 [32] valid_0's l1: 1.62521e+06 [33] valid_0's l1: 1.6268e+06 [34] valid_0's l1: 1.62937e+06 [35] valid_0's l1: 1.62197e+06 [36] valid_0's l1: 1.62251e+06 [37] valid_0's l1: 1.61791e+06 [38] valid_0's l1: 1.6191e+06 [39] valid_0's l1: 1.62166e+06 [40] valid_0's l1: 1.61971e+06 [41] valid_0's l1: 1.62036e+06 [42] valid_0's l1: 1.62022e+06 [43] valid_0's l1: 1.62122e+06 [44] valid_0's l1: 1.62306e+06 [45] valid_0's l1: 1.62404e+06 [46] valid_0's l1: 1.62464e+06 [47] valid_0's l1: 1.62581e+06 [48] valid_0's l1: 1.6233e+06 [49] valid_0's l1: 1.62433e+06 [50] valid_0's l1: 1.62647e+06 [51] valid_0's l1: 1.62283e+06 [52] valid_0's l1: 1.62738e+06 [53] valid_0's l1: 1.62974e+06 [54] valid_0's l1: 1.63427e+06 [55] valid_0's l1: 1.63703e+06 [56] valid_0's l1: 1.63911e+06 [57] valid_0's l1: 1.6396e+06 [58] valid_0's l1: 1.63766e+06 [59] valid_0's l1: 1.64045e+06 [60] valid_0's l1: 1.64282e+06 [61] valid_0's l1: 1.64267e+06 [62] valid_0's l1: 1.64467e+06 [63] valid_0's l1: 1.64473e+06 [64] valid_0's l1: 1.64545e+06 [65] valid_0's l1: 1.64774e+06 [66] valid_0's l1: 1.64728e+06 [67] valid_0's l1: 1.64694e+06 Early stopping, best iteration is: [37] valid_0's l1: 1.61791e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.82314e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.53885e+06 [3] valid_0's l1: 3.28346e+06 [4] valid_0's l1: 3.05156e+06 [5] valid_0's l1: 2.85542e+06 [6] valid_0's l1: 2.75925e+06 [7] valid_0's l1: 2.66959e+06 [8] valid_0's l1: 2.52656e+06 [9] valid_0's l1: 2.40248e+06 [10] valid_0's l1: 2.29316e+06 [11] valid_0's l1: 2.1974e+06 [12] valid_0's l1: 2.15027e+06 [13] valid_0's l1: 2.07389e+06 [14] valid_0's l1: 2.04006e+06 [15] valid_0's l1: 1.98279e+06 [16] valid_0's l1: 1.93007e+06 [17] valid_0's l1: 1.88619e+06 [18] valid_0's l1: 1.84794e+06 [19] valid_0's l1: 1.8186e+06 [20] valid_0's l1: 1.78666e+06 [21] valid_0's l1: 1.76808e+06 [22] valid_0's l1: 1.75232e+06 [23] valid_0's l1: 1.73378e+06 [24] valid_0's l1: 1.71863e+06 [25] valid_0's l1: 1.70628e+06 [26] valid_0's l1: 1.69601e+06 [27] valid_0's l1: 1.68197e+06 [28] valid_0's l1: 1.67391e+06 [29] valid_0's l1: 1.66586e+06 [30] valid_0's l1: 1.66154e+06 [31] valid_0's l1: 1.65638e+06 [32] valid_0's l1: 1.65255e+06 [33] valid_0's l1: 1.65111e+06 [34] valid_0's l1: 1.65118e+06 [35] valid_0's l1: 1.64782e+06 [36] valid_0's l1: 1.64348e+06 [37] valid_0's l1: 1.64234e+06 [38] valid_0's l1: 1.6437e+06 [39] valid_0's l1: 1.64265e+06 [40] valid_0's l1: 1.64379e+06 [41] valid_0's l1: 1.64313e+06 [42] valid_0's l1: 1.6418e+06 [43] valid_0's l1: 1.64361e+06 [44] valid_0's l1: 1.64218e+06 [45] valid_0's l1: 1.64402e+06 [46] valid_0's l1: 1.64146e+06 [47] valid_0's l1: 1.64262e+06 [48] valid_0's l1: 1.64187e+06 [49] valid_0's l1: 1.64002e+06 [50] valid_0's l1: 1.63854e+06 [51] valid_0's l1: 1.63858e+06 [52] valid_0's l1: 1.63825e+06 [53] valid_0's l1: 1.64072e+06 [54] valid_0's l1: 1.64255e+06 [55] valid_0's l1: 1.64161e+06 [56] valid_0's l1: 1.64266e+06 [57] valid_0's l1: 1.64504e+06 [58] valid_0's l1: 1.64487e+06 [59] valid_0's l1: 1.64461e+06 [60] valid_0's l1: 1.6457e+06 [61] valid_0's l1: 1.64668e+06 [62] valid_0's l1: 1.64771e+06 [63] valid_0's l1: 1.64831e+06 [64] valid_0's l1: 1.64711e+06 [65] valid_0's l1: 1.64739e+06 [66] valid_0's l1: 1.6483e+06 [67] valid_0's l1: 1.64816e+06 [68] valid_0's l1: 1.64978e+06 [69] valid_0's l1: 1.65005e+06 [70] valid_0's l1: 1.65012e+06 [71] valid_0's l1: 1.65122e+06 [72] valid_0's l1: 1.6506e+06 [73] valid_0's l1: 1.6509e+06 [74] valid_0's l1: 1.6501e+06 [75] valid_0's l1: 1.65227e+06 [76] valid_0's l1: 1.65293e+06 [77] valid_0's l1: 1.65491e+06 [78] valid_0's l1: 1.65499e+06 [79] valid_0's l1: 1.65567e+06 [80] valid_0's l1: 1.65697e+06 [81] valid_0's l1: 1.65648e+06 [82] valid_0's l1: 1.65633e+06 Early stopping, best iteration is: [52] valid_0's l1: 1.63825e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.90347e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.60447e+06 [3] valid_0's l1: 3.33163e+06 [4] valid_0's l1: 3.09336e+06 [5] valid_0's l1: 2.88614e+06 [6] valid_0's l1: 2.77812e+06 [7] valid_0's l1: 2.68595e+06 [8] valid_0's l1: 2.53776e+06 [9] valid_0's l1: 2.40858e+06 [10] valid_0's l1: 2.29804e+06 [11] valid_0's l1: 2.20018e+06 [12] valid_0's l1: 2.14893e+06 [13] valid_0's l1: 2.07158e+06 [14] valid_0's l1: 2.03795e+06 [15] valid_0's l1: 1.98575e+06 [16] valid_0's l1: 1.93925e+06 [17] valid_0's l1: 1.89833e+06 [18] valid_0's l1: 1.85799e+06 [19] valid_0's l1: 1.81604e+06 [20] valid_0's l1: 1.78718e+06 [21] valid_0's l1: 1.76339e+06 [22] valid_0's l1: 1.73882e+06 [23] valid_0's l1: 1.71904e+06 [24] valid_0's l1: 1.70315e+06 [25] valid_0's l1: 1.68543e+06 [26] valid_0's l1: 1.67107e+06 [27] valid_0's l1: 1.66136e+06 [28] valid_0's l1: 1.65118e+06 [29] valid_0's l1: 1.64208e+06 [30] valid_0's l1: 1.63366e+06 [31] valid_0's l1: 1.63059e+06 [32] valid_0's l1: 1.62535e+06 [33] valid_0's l1: 1.62527e+06 [34] valid_0's l1: 1.62184e+06 [35] valid_0's l1: 1.62203e+06 [36] valid_0's l1: 1.62009e+06 [37] valid_0's l1: 1.61825e+06 [38] valid_0's l1: 1.61759e+06 [39] valid_0's l1: 1.61438e+06 [40] valid_0's l1: 1.61596e+06 [41] valid_0's l1: 1.61631e+06 [42] valid_0's l1: 1.61543e+06 [43] valid_0's l1: 1.61522e+06 [44] valid_0's l1: 1.61456e+06 [45] valid_0's l1: 1.61175e+06 [46] valid_0's l1: 1.61233e+06 [47] valid_0's l1: 1.61393e+06 [48] valid_0's l1: 1.61366e+06 [49] valid_0's l1: 1.61534e+06 [50] valid_0's l1: 1.61645e+06 [51] valid_0's l1: 1.61485e+06 [52] valid_0's l1: 1.61601e+06 [53] valid_0's l1: 1.61818e+06 [54] valid_0's l1: 1.61939e+06 [55] valid_0's l1: 1.61976e+06 [56] valid_0's l1: 1.61974e+06 [57] valid_0's l1: 1.62078e+06 [58] valid_0's l1: 1.62052e+06 [59] valid_0's l1: 1.62096e+06 [60] valid_0's l1: 1.61909e+06 [61] valid_0's l1: 1.62111e+06 [62] valid_0's l1: 1.62204e+06 [63] valid_0's l1: 1.624e+06 [64] valid_0's l1: 1.62425e+06 [65] valid_0's l1: 1.62319e+06 [66] valid_0's l1: 1.6254e+06 [67] valid_0's l1: 1.62587e+06 [68] valid_0's l1: 1.6266e+06 [69] valid_0's l1: 1.62549e+06 [70] valid_0's l1: 1.62726e+06 [71] valid_0's l1: 1.62715e+06 [72] valid_0's l1: 1.62843e+06 [73] valid_0's l1: 1.631e+06 [74] valid_0's l1: 1.62983e+06 [75] valid_0's l1: 1.63112e+06 Early stopping, best iteration is: [45] valid_0's l1: 1.61175e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.8804e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.58147e+06 [3] valid_0's l1: 3.31561e+06 [4] valid_0's l1: 3.08939e+06 [5] valid_0's l1: 2.89077e+06 [6] valid_0's l1: 2.79162e+06 [7] valid_0's l1: 2.70318e+06 [8] valid_0's l1: 2.55683e+06 [9] valid_0's l1: 2.42573e+06 [10] valid_0's l1: 2.31887e+06 [11] valid_0's l1: 2.22359e+06 [12] valid_0's l1: 2.18024e+06 [13] valid_0's l1: 2.09433e+06 [14] valid_0's l1: 2.06667e+06 [15] valid_0's l1: 2.0008e+06 [16] valid_0's l1: 1.94363e+06 [17] valid_0's l1: 1.89901e+06 [18] valid_0's l1: 1.85718e+06 [19] valid_0's l1: 1.81714e+06 [20] valid_0's l1: 1.78493e+06 [21] valid_0's l1: 1.75613e+06 [22] valid_0's l1: 1.73142e+06 [23] valid_0's l1: 1.71209e+06 [24] valid_0's l1: 1.69322e+06 [25] valid_0's l1: 1.67855e+06 [26] valid_0's l1: 1.66736e+06 [27] valid_0's l1: 1.66042e+06 [28] valid_0's l1: 1.64903e+06 [29] valid_0's l1: 1.64043e+06 [30] valid_0's l1: 1.63364e+06 [31] valid_0's l1: 1.62925e+06 [32] valid_0's l1: 1.62312e+06 [33] valid_0's l1: 1.62148e+06 [34] valid_0's l1: 1.6214e+06 [35] valid_0's l1: 1.61554e+06 [36] valid_0's l1: 1.61147e+06 [37] valid_0's l1: 1.61104e+06 [38] valid_0's l1: 1.60897e+06 [39] valid_0's l1: 1.61109e+06 [40] valid_0's l1: 1.60755e+06 [41] valid_0's l1: 1.60865e+06 [42] valid_0's l1: 1.61138e+06 [43] valid_0's l1: 1.61081e+06 [44] valid_0's l1: 1.61109e+06 [45] valid_0's l1: 1.61105e+06 [46] valid_0's l1: 1.6129e+06 [47] valid_0's l1: 1.61142e+06 [48] valid_0's l1: 1.61223e+06 [49] valid_0's l1: 1.61085e+06 [50] valid_0's l1: 1.6142e+06 [51] valid_0's l1: 1.61623e+06 [52] valid_0's l1: 1.61796e+06 [53] valid_0's l1: 1.6188e+06 [54] valid_0's l1: 1.62176e+06 [55] valid_0's l1: 1.62242e+06 [56] valid_0's l1: 1.62088e+06 [57] valid_0's l1: 1.62222e+06 [58] valid_0's l1: 1.62347e+06 [59] valid_0's l1: 1.62433e+06 [60] valid_0's l1: 1.62428e+06 [61] valid_0's l1: 1.6274e+06 [62] valid_0's l1: 1.62864e+06 [63] valid_0's l1: 1.62993e+06 [64] valid_0's l1: 1.62965e+06 [65] valid_0's l1: 1.63218e+06 [66] valid_0's l1: 1.63269e+06 [67] valid_0's l1: 1.63347e+06 [68] valid_0's l1: 1.63408e+06 [69] valid_0's l1: 1.63432e+06 [70] valid_0's l1: 1.63412e+06 Early stopping, best iteration is: [40] valid_0's l1: 1.60755e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.82234e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.53738e+06 [3] valid_0's l1: 3.28278e+06 [4] valid_0's l1: 3.05055e+06 [5] valid_0's l1: 2.85395e+06 [6] valid_0's l1: 2.76027e+06 [7] valid_0's l1: 2.67207e+06 [8] valid_0's l1: 2.52479e+06 [9] valid_0's l1: 2.39693e+06 [10] valid_0's l1: 2.2844e+06 [11] valid_0's l1: 2.18771e+06 [12] valid_0's l1: 2.13956e+06 [13] valid_0's l1: 2.06551e+06 [14] valid_0's l1: 2.03037e+06 [15] valid_0's l1: 1.97266e+06 [16] valid_0's l1: 1.92277e+06 [17] valid_0's l1: 1.88071e+06 [18] valid_0's l1: 1.84012e+06 [19] valid_0's l1: 1.80914e+06 [20] valid_0's l1: 1.78341e+06 [21] valid_0's l1: 1.75977e+06 [22] valid_0's l1: 1.73938e+06 [23] valid_0's l1: 1.71884e+06 [24] valid_0's l1: 1.704e+06 [25] valid_0's l1: 1.68982e+06 [26] valid_0's l1: 1.67853e+06 [27] valid_0's l1: 1.66707e+06 [28] valid_0's l1: 1.6575e+06 [29] valid_0's l1: 1.64879e+06 [30] valid_0's l1: 1.64292e+06 [31] valid_0's l1: 1.63945e+06 [32] valid_0's l1: 1.6373e+06 [33] valid_0's l1: 1.63527e+06 [34] valid_0's l1: 1.63174e+06 [35] valid_0's l1: 1.6321e+06 [36] valid_0's l1: 1.62921e+06 [37] valid_0's l1: 1.63137e+06 [38] valid_0's l1: 1.62857e+06 [39] valid_0's l1: 1.62869e+06 [40] valid_0's l1: 1.62636e+06 [41] valid_0's l1: 1.62635e+06 [42] valid_0's l1: 1.62345e+06 [43] valid_0's l1: 1.62422e+06 [44] valid_0's l1: 1.62453e+06 [45] valid_0's l1: 1.62611e+06 [46] valid_0's l1: 1.6248e+06 [47] valid_0's l1: 1.62704e+06 [48] valid_0's l1: 1.62781e+06 [49] valid_0's l1: 1.6272e+06 [50] valid_0's l1: 1.62614e+06 [51] valid_0's l1: 1.62683e+06 [52] valid_0's l1: 1.62589e+06 [53] valid_0's l1: 1.62841e+06 [54] valid_0's l1: 1.62995e+06 [55] valid_0's l1: 1.63114e+06 [56] valid_0's l1: 1.63104e+06 [57] valid_0's l1: 1.63222e+06 [58] valid_0's l1: 1.63222e+06 [59] valid_0's l1: 1.63304e+06 [60] valid_0's l1: 1.63325e+06 [61] valid_0's l1: 1.63288e+06 [62] valid_0's l1: 1.63368e+06 [63] valid_0's l1: 1.634e+06 [64] valid_0's l1: 1.63577e+06 [65] valid_0's l1: 1.63388e+06 [66] valid_0's l1: 1.63492e+06 [67] valid_0's l1: 1.63456e+06 [68] valid_0's l1: 1.63418e+06 [69] valid_0's l1: 1.63338e+06 [70] valid_0's l1: 1.6334e+06 [71] valid_0's l1: 1.63182e+06 [72] valid_0's l1: 1.63426e+06 Early stopping, best iteration is: [42] valid_0's l1: 1.62345e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.90331e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.60346e+06 [3] valid_0's l1: 3.33147e+06 [4] valid_0's l1: 3.0943e+06 [5] valid_0's l1: 2.88691e+06 [6] valid_0's l1: 2.77737e+06 [7] valid_0's l1: 2.68585e+06 [8] valid_0's l1: 2.53803e+06 [9] valid_0's l1: 2.4083e+06 [10] valid_0's l1: 2.29773e+06 [11] valid_0's l1: 2.19713e+06 [12] valid_0's l1: 2.14466e+06 [13] valid_0's l1: 2.06369e+06 [14] valid_0's l1: 2.03015e+06 [15] valid_0's l1: 1.97551e+06 [16] valid_0's l1: 1.92629e+06 [17] valid_0's l1: 1.88563e+06 [18] valid_0's l1: 1.84773e+06 [19] valid_0's l1: 1.81126e+06 [20] valid_0's l1: 1.7812e+06 [21] valid_0's l1: 1.75382e+06 [22] valid_0's l1: 1.73287e+06 [23] valid_0's l1: 1.71452e+06 [24] valid_0's l1: 1.69565e+06 [25] valid_0's l1: 1.68183e+06 [26] valid_0's l1: 1.667e+06 [27] valid_0's l1: 1.6531e+06 [28] valid_0's l1: 1.64525e+06 [29] valid_0's l1: 1.63498e+06 [30] valid_0's l1: 1.62945e+06 [31] valid_0's l1: 1.62718e+06 [32] valid_0's l1: 1.61879e+06 [33] valid_0's l1: 1.61602e+06 [34] valid_0's l1: 1.6127e+06 [35] valid_0's l1: 1.60762e+06 [36] valid_0's l1: 1.60376e+06 [37] valid_0's l1: 1.60473e+06 [38] valid_0's l1: 1.60411e+06 [39] valid_0's l1: 1.6067e+06 [40] valid_0's l1: 1.60682e+06 [41] valid_0's l1: 1.60711e+06 [42] valid_0's l1: 1.60511e+06 [43] valid_0's l1: 1.60621e+06 [44] valid_0's l1: 1.60682e+06 [45] valid_0's l1: 1.60734e+06 [46] valid_0's l1: 1.60853e+06 [47] valid_0's l1: 1.61095e+06 [48] valid_0's l1: 1.61156e+06 [49] valid_0's l1: 1.61278e+06 [50] valid_0's l1: 1.61521e+06 [51] valid_0's l1: 1.61715e+06 [52] valid_0's l1: 1.61574e+06 [53] valid_0's l1: 1.61809e+06 [54] valid_0's l1: 1.61949e+06 [55] valid_0's l1: 1.61926e+06 [56] valid_0's l1: 1.61998e+06 [57] valid_0's l1: 1.62428e+06 [58] valid_0's l1: 1.62571e+06 [59] valid_0's l1: 1.62745e+06 [60] valid_0's l1: 1.6269e+06 [61] valid_0's l1: 1.62842e+06 [62] valid_0's l1: 1.62883e+06 [63] valid_0's l1: 1.62782e+06 [64] valid_0's l1: 1.62625e+06 [65] valid_0's l1: 1.62601e+06 [66] valid_0's l1: 1.6253e+06 Early stopping, best iteration is: [36] valid_0's l1: 1.60376e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.88063e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.58111e+06 [3] valid_0's l1: 3.31471e+06 [4] valid_0's l1: 3.08971e+06 [5] valid_0's l1: 2.89137e+06 [6] valid_0's l1: 2.79091e+06 [7] valid_0's l1: 2.7016e+06 [8] valid_0's l1: 2.5545e+06 [9] valid_0's l1: 2.42672e+06 [10] valid_0's l1: 2.31773e+06 [11] valid_0's l1: 2.22329e+06 [12] valid_0's l1: 2.17855e+06 [13] valid_0's l1: 2.09992e+06 [14] valid_0's l1: 2.07693e+06 [15] valid_0's l1: 2.00991e+06 [16] valid_0's l1: 1.95164e+06 [17] valid_0's l1: 1.90423e+06 [18] valid_0's l1: 1.86449e+06 [19] valid_0's l1: 1.82493e+06 [20] valid_0's l1: 1.79157e+06 [21] valid_0's l1: 1.75899e+06 [22] valid_0's l1: 1.7361e+06 [23] valid_0's l1: 1.71289e+06 [24] valid_0's l1: 1.69566e+06 [25] valid_0's l1: 1.68045e+06 [26] valid_0's l1: 1.66649e+06 [27] valid_0's l1: 1.65602e+06 [28] valid_0's l1: 1.65022e+06 [29] valid_0's l1: 1.64358e+06 [30] valid_0's l1: 1.63472e+06 [31] valid_0's l1: 1.63274e+06 [32] valid_0's l1: 1.62521e+06 [33] valid_0's l1: 1.6268e+06 [34] valid_0's l1: 1.62937e+06 [35] valid_0's l1: 1.62197e+06 [36] valid_0's l1: 1.62251e+06 [37] valid_0's l1: 1.61791e+06 [38] valid_0's l1: 1.6191e+06 [39] valid_0's l1: 1.62166e+06 [40] valid_0's l1: 1.61971e+06 [41] valid_0's l1: 1.62036e+06 [42] valid_0's l1: 1.62022e+06 [43] valid_0's l1: 1.62122e+06 [44] valid_0's l1: 1.62306e+06 [45] valid_0's l1: 1.62404e+06 [46] valid_0's l1: 1.62464e+06 [47] valid_0's l1: 1.62581e+06 [48] valid_0's l1: 1.6233e+06 [49] valid_0's l1: 1.62433e+06 [50] valid_0's l1: 1.62647e+06 [51] valid_0's l1: 1.62283e+06 [52] valid_0's l1: 1.62738e+06 [53] valid_0's l1: 1.62974e+06 [54] valid_0's l1: 1.63427e+06 [55] valid_0's l1: 1.63703e+06 [56] valid_0's l1: 1.63911e+06 [57] valid_0's l1: 1.6396e+06 [58] valid_0's l1: 1.63766e+06 [59] valid_0's l1: 1.64045e+06 [60] valid_0's l1: 1.64282e+06 [61] valid_0's l1: 1.64267e+06 [62] valid_0's l1: 1.64467e+06 [63] valid_0's l1: 1.64473e+06 [64] valid_0's l1: 1.64545e+06 [65] valid_0's l1: 1.64774e+06 [66] valid_0's l1: 1.64728e+06 [67] valid_0's l1: 1.64694e+06 Early stopping, best iteration is: [37] valid_0's l1: 1.61791e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.82314e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.53885e+06 [3] valid_0's l1: 3.28346e+06 [4] valid_0's l1: 3.05156e+06 [5] valid_0's l1: 2.85542e+06 [6] valid_0's l1: 2.75925e+06 [7] valid_0's l1: 2.66959e+06 [8] valid_0's l1: 2.52656e+06 [9] valid_0's l1: 2.40248e+06 [10] valid_0's l1: 2.29316e+06 [11] valid_0's l1: 2.1974e+06 [12] valid_0's l1: 2.15027e+06 [13] valid_0's l1: 2.07389e+06 [14] valid_0's l1: 2.04006e+06 [15] valid_0's l1: 1.98279e+06 [16] valid_0's l1: 1.93007e+06 [17] valid_0's l1: 1.88619e+06 [18] valid_0's l1: 1.84794e+06 [19] valid_0's l1: 1.8186e+06 [20] valid_0's l1: 1.78666e+06 [21] valid_0's l1: 1.76808e+06 [22] valid_0's l1: 1.75232e+06 [23] valid_0's l1: 1.73378e+06 [24] valid_0's l1: 1.71863e+06 [25] valid_0's l1: 1.70628e+06 [26] valid_0's l1: 1.69601e+06 [27] valid_0's l1: 1.68197e+06 [28] valid_0's l1: 1.67391e+06 [29] valid_0's l1: 1.66586e+06 [30] valid_0's l1: 1.66154e+06 [31] valid_0's l1: 1.65638e+06 [32] valid_0's l1: 1.65255e+06 [33] valid_0's l1: 1.65111e+06 [34] valid_0's l1: 1.65118e+06 [35] valid_0's l1: 1.64782e+06 [36] valid_0's l1: 1.64348e+06 [37] valid_0's l1: 1.64234e+06 [38] valid_0's l1: 1.6437e+06 [39] valid_0's l1: 1.64265e+06 [40] valid_0's l1: 1.64379e+06 [41] valid_0's l1: 1.64313e+06 [42] valid_0's l1: 1.6418e+06 [43] valid_0's l1: 1.64361e+06 [44] valid_0's l1: 1.64218e+06 [45] valid_0's l1: 1.64402e+06 [46] valid_0's l1: 1.64146e+06 [47] valid_0's l1: 1.64262e+06 [48] valid_0's l1: 1.64187e+06 [49] valid_0's l1: 1.64002e+06 [50] valid_0's l1: 1.63854e+06 [51] valid_0's l1: 1.63858e+06 [52] valid_0's l1: 1.63825e+06 [53] valid_0's l1: 1.64072e+06 [54] valid_0's l1: 1.64255e+06 [55] valid_0's l1: 1.64161e+06 [56] valid_0's l1: 1.64266e+06 [57] valid_0's l1: 1.64504e+06 [58] valid_0's l1: 1.64487e+06 [59] valid_0's l1: 1.64461e+06 [60] valid_0's l1: 1.6457e+06 [61] valid_0's l1: 1.64668e+06 [62] valid_0's l1: 1.64771e+06 [63] valid_0's l1: 1.64831e+06 [64] valid_0's l1: 1.64711e+06 [65] valid_0's l1: 1.64739e+06 [66] valid_0's l1: 1.6483e+06 [67] valid_0's l1: 1.64816e+06 [68] valid_0's l1: 1.64978e+06 [69] valid_0's l1: 1.65005e+06 [70] valid_0's l1: 1.65012e+06 [71] valid_0's l1: 1.65122e+06 [72] valid_0's l1: 1.6506e+06 [73] valid_0's l1: 1.6509e+06 [74] valid_0's l1: 1.6501e+06 [75] valid_0's l1: 1.65227e+06 [76] valid_0's l1: 1.65293e+06 [77] valid_0's l1: 1.65491e+06 [78] valid_0's l1: 1.65499e+06 [79] valid_0's l1: 1.65567e+06 [80] valid_0's l1: 1.65697e+06 [81] valid_0's l1: 1.65648e+06 [82] valid_0's l1: 1.65633e+06 Early stopping, best iteration is: [52] valid_0's l1: 1.63825e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.90347e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.60447e+06 [3] valid_0's l1: 3.33163e+06 [4] valid_0's l1: 3.09336e+06 [5] valid_0's l1: 2.88614e+06 [6] valid_0's l1: 2.77812e+06 [7] valid_0's l1: 2.68595e+06 [8] valid_0's l1: 2.53776e+06 [9] valid_0's l1: 2.40858e+06 [10] valid_0's l1: 2.29804e+06 [11] valid_0's l1: 2.20018e+06 [12] valid_0's l1: 2.14893e+06 [13] valid_0's l1: 2.07158e+06 [14] valid_0's l1: 2.03795e+06 [15] valid_0's l1: 1.98575e+06 [16] valid_0's l1: 1.93925e+06 [17] valid_0's l1: 1.89833e+06 [18] valid_0's l1: 1.85799e+06 [19] valid_0's l1: 1.81604e+06 [20] valid_0's l1: 1.78718e+06 [21] valid_0's l1: 1.76339e+06 [22] valid_0's l1: 1.73882e+06 [23] valid_0's l1: 1.71904e+06 [24] valid_0's l1: 1.70315e+06 [25] valid_0's l1: 1.68543e+06 [26] valid_0's l1: 1.67107e+06 [27] valid_0's l1: 1.66136e+06 [28] valid_0's l1: 1.65118e+06 [29] valid_0's l1: 1.64208e+06 [30] valid_0's l1: 1.63366e+06 [31] valid_0's l1: 1.63059e+06 [32] valid_0's l1: 1.62535e+06 [33] valid_0's l1: 1.62527e+06 [34] valid_0's l1: 1.62184e+06 [35] valid_0's l1: 1.62203e+06 [36] valid_0's l1: 1.62009e+06 [37] valid_0's l1: 1.61825e+06 [38] valid_0's l1: 1.61759e+06 [39] valid_0's l1: 1.61438e+06 [40] valid_0's l1: 1.61596e+06 [41] valid_0's l1: 1.61631e+06 [42] valid_0's l1: 1.61543e+06 [43] valid_0's l1: 1.61522e+06 [44] valid_0's l1: 1.61456e+06 [45] valid_0's l1: 1.61175e+06 [46] valid_0's l1: 1.61233e+06 [47] valid_0's l1: 1.61393e+06 [48] valid_0's l1: 1.61366e+06 [49] valid_0's l1: 1.61534e+06 [50] valid_0's l1: 1.61645e+06 [51] valid_0's l1: 1.61485e+06 [52] valid_0's l1: 1.61601e+06 [53] valid_0's l1: 1.61818e+06 [54] valid_0's l1: 1.61939e+06 [55] valid_0's l1: 1.61976e+06 [56] valid_0's l1: 1.61974e+06 [57] valid_0's l1: 1.62078e+06 [58] valid_0's l1: 1.62052e+06 [59] valid_0's l1: 1.62096e+06 [60] valid_0's l1: 1.61909e+06 [61] valid_0's l1: 1.62111e+06 [62] valid_0's l1: 1.62204e+06 [63] valid_0's l1: 1.624e+06 [64] valid_0's l1: 1.62425e+06 [65] valid_0's l1: 1.62319e+06 [66] valid_0's l1: 1.6254e+06 [67] valid_0's l1: 1.62587e+06 [68] valid_0's l1: 1.6266e+06 [69] valid_0's l1: 1.62549e+06 [70] valid_0's l1: 1.62726e+06 [71] valid_0's l1: 1.62715e+06 [72] valid_0's l1: 1.62843e+06 [73] valid_0's l1: 1.631e+06 [74] valid_0's l1: 1.62983e+06 [75] valid_0's l1: 1.63112e+06 Early stopping, best iteration is: [45] valid_0's l1: 1.61175e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.90081e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.59582e+06 [3] valid_0's l1: 3.33132e+06 [4] valid_0's l1: 3.098e+06 [5] valid_0's l1: 2.89114e+06 [6] valid_0's l1: 2.80556e+06 [7] valid_0's l1: 2.72592e+06 [8] valid_0's l1: 2.58085e+06 [9] valid_0's l1: 2.45021e+06 [10] valid_0's l1: 2.33527e+06 [11] valid_0's l1: 2.2391e+06 [12] valid_0's l1: 2.19693e+06 [13] valid_0's l1: 2.11329e+06 [14] valid_0's l1: 2.09066e+06 [15] valid_0's l1: 2.0198e+06 [16] valid_0's l1: 1.95972e+06 [17] valid_0's l1: 1.90723e+06 [18] valid_0's l1: 1.86494e+06 [19] valid_0's l1: 1.82292e+06 [20] valid_0's l1: 1.78873e+06 [21] valid_0's l1: 1.75883e+06 [22] valid_0's l1: 1.73505e+06 [23] valid_0's l1: 1.71207e+06 [24] valid_0's l1: 1.6975e+06 [25] valid_0's l1: 1.67955e+06 [26] valid_0's l1: 1.66097e+06 [27] valid_0's l1: 1.64628e+06 [28] valid_0's l1: 1.63646e+06 [29] valid_0's l1: 1.62795e+06 [30] valid_0's l1: 1.61504e+06 [31] valid_0's l1: 1.61319e+06 [32] valid_0's l1: 1.60404e+06 [33] valid_0's l1: 1.59766e+06 [34] valid_0's l1: 1.59666e+06 [35] valid_0's l1: 1.59503e+06 [36] valid_0's l1: 1.5944e+06 [37] valid_0's l1: 1.59336e+06 [38] valid_0's l1: 1.59277e+06 [39] valid_0's l1: 1.58696e+06 [40] valid_0's l1: 1.58668e+06 [41] valid_0's l1: 1.58367e+06 [42] valid_0's l1: 1.58363e+06 [43] valid_0's l1: 1.58274e+06 [44] valid_0's l1: 1.57959e+06 [45] valid_0's l1: 1.58042e+06 [46] valid_0's l1: 1.58166e+06 [47] valid_0's l1: 1.57879e+06 [48] valid_0's l1: 1.57911e+06 [49] valid_0's l1: 1.58157e+06 [50] valid_0's l1: 1.58284e+06 [51] valid_0's l1: 1.58312e+06 [52] valid_0's l1: 1.57803e+06 [53] valid_0's l1: 1.57922e+06 [54] valid_0's l1: 1.58002e+06 [55] valid_0's l1: 1.58115e+06 [56] valid_0's l1: 1.58287e+06 [57] valid_0's l1: 1.58232e+06 [58] valid_0's l1: 1.58297e+06 [59] valid_0's l1: 1.58295e+06 [60] valid_0's l1: 1.58504e+06 [61] valid_0's l1: 1.58515e+06 [62] valid_0's l1: 1.58561e+06 [63] valid_0's l1: 1.58709e+06 [64] valid_0's l1: 1.58507e+06 [65] valid_0's l1: 1.58536e+06 [66] valid_0's l1: 1.58432e+06 [67] valid_0's l1: 1.58613e+06 [68] valid_0's l1: 1.58538e+06 [69] valid_0's l1: 1.58021e+06 [70] valid_0's l1: 1.5804e+06 [71] valid_0's l1: 1.58138e+06 [72] valid_0's l1: 1.58121e+06 [73] valid_0's l1: 1.58254e+06 [74] valid_0's l1: 1.58233e+06 [75] valid_0's l1: 1.58032e+06 [76] valid_0's l1: 1.58063e+06 [77] valid_0's l1: 1.58148e+06 [78] valid_0's l1: 1.57924e+06 [79] valid_0's l1: 1.58047e+06 [80] valid_0's l1: 1.58135e+06 [81] valid_0's l1: 1.57956e+06 [82] valid_0's l1: 1.57943e+06 Early stopping, best iteration is: [52] valid_0's l1: 1.57803e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.84197e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.55342e+06 [3] valid_0's l1: 3.29726e+06 [4] valid_0's l1: 3.06367e+06 [5] valid_0's l1: 2.86201e+06 [6] valid_0's l1: 2.77263e+06 [7] valid_0's l1: 2.69511e+06 [8] valid_0's l1: 2.54647e+06 [9] valid_0's l1: 2.41522e+06 [10] valid_0's l1: 2.30494e+06 [11] valid_0's l1: 2.20889e+06 [12] valid_0's l1: 2.16019e+06 [13] valid_0's l1: 2.08103e+06 [14] valid_0's l1: 2.05287e+06 [15] valid_0's l1: 1.98986e+06 [16] valid_0's l1: 1.93642e+06 [17] valid_0's l1: 1.89289e+06 [18] valid_0's l1: 1.8552e+06 [19] valid_0's l1: 1.82202e+06 [20] valid_0's l1: 1.79212e+06 [21] valid_0's l1: 1.76743e+06 [22] valid_0's l1: 1.74428e+06 [23] valid_0's l1: 1.72953e+06 [24] valid_0's l1: 1.71457e+06 [25] valid_0's l1: 1.70052e+06 [26] valid_0's l1: 1.68713e+06 [27] valid_0's l1: 1.67176e+06 [28] valid_0's l1: 1.6637e+06 [29] valid_0's l1: 1.6565e+06 [30] valid_0's l1: 1.64877e+06 [31] valid_0's l1: 1.63661e+06 [32] valid_0's l1: 1.62954e+06 [33] valid_0's l1: 1.62726e+06 [34] valid_0's l1: 1.61977e+06 [35] valid_0's l1: 1.61731e+06 [36] valid_0's l1: 1.60926e+06 [37] valid_0's l1: 1.60815e+06 [38] valid_0's l1: 1.60559e+06 [39] valid_0's l1: 1.60279e+06 [40] valid_0's l1: 1.6027e+06 [41] valid_0's l1: 1.59799e+06 [42] valid_0's l1: 1.59741e+06 [43] valid_0's l1: 1.59672e+06 [44] valid_0's l1: 1.5961e+06 [45] valid_0's l1: 1.59397e+06 [46] valid_0's l1: 1.59023e+06 [47] valid_0's l1: 1.58949e+06 [48] valid_0's l1: 1.58952e+06 [49] valid_0's l1: 1.58677e+06 [50] valid_0's l1: 1.58555e+06 [51] valid_0's l1: 1.58606e+06 [52] valid_0's l1: 1.58418e+06 [53] valid_0's l1: 1.58507e+06 [54] valid_0's l1: 1.58497e+06 [55] valid_0's l1: 1.58581e+06 [56] valid_0's l1: 1.58421e+06 [57] valid_0's l1: 1.58225e+06 [58] valid_0's l1: 1.5824e+06 [59] valid_0's l1: 1.58158e+06 [60] valid_0's l1: 1.58005e+06 [61] valid_0's l1: 1.58137e+06 [62] valid_0's l1: 1.58155e+06 [63] valid_0's l1: 1.58091e+06 [64] valid_0's l1: 1.58157e+06 [65] valid_0's l1: 1.58289e+06 [66] valid_0's l1: 1.58426e+06 [67] valid_0's l1: 1.5831e+06 [68] valid_0's l1: 1.58122e+06 [69] valid_0's l1: 1.58157e+06 [70] valid_0's l1: 1.58213e+06 [71] valid_0's l1: 1.58303e+06 [72] valid_0's l1: 1.58277e+06 [73] valid_0's l1: 1.58444e+06 [74] valid_0's l1: 1.58325e+06 [75] valid_0's l1: 1.58386e+06 [76] valid_0's l1: 1.58379e+06 [77] valid_0's l1: 1.58402e+06 [78] valid_0's l1: 1.58249e+06 [79] valid_0's l1: 1.58142e+06 [80] valid_0's l1: 1.58035e+06 [81] valid_0's l1: 1.5809e+06 [82] valid_0's l1: 1.57963e+06 [83] valid_0's l1: 1.58035e+06 [84] valid_0's l1: 1.57931e+06 [85] valid_0's l1: 1.58015e+06 [86] valid_0's l1: 1.57969e+06 [87] valid_0's l1: 1.57984e+06 [88] valid_0's l1: 1.58188e+06 [89] valid_0's l1: 1.58304e+06 [90] valid_0's l1: 1.58135e+06 [91] valid_0's l1: 1.58232e+06 [92] valid_0's l1: 1.58257e+06 [93] valid_0's l1: 1.58241e+06 [94] valid_0's l1: 1.58202e+06 [95] valid_0's l1: 1.5829e+06 [96] valid_0's l1: 1.58211e+06 [97] valid_0's l1: 1.58216e+06 [98] valid_0's l1: 1.58011e+06 [99] valid_0's l1: 1.58153e+06 [100] valid_0's l1: 1.58087e+06 [101] valid_0's l1: 1.58134e+06 [102] valid_0's l1: 1.58232e+06 [103] valid_0's l1: 1.58281e+06 [104] valid_0's l1: 1.58261e+06 [105] valid_0's l1: 1.58244e+06 [106] valid_0's l1: 1.58099e+06 [107] valid_0's l1: 1.58132e+06 [108] valid_0's l1: 1.58327e+06 [109] valid_0's l1: 1.58179e+06 [110] valid_0's l1: 1.58224e+06 [111] valid_0's l1: 1.58337e+06 [112] valid_0's l1: 1.58372e+06 [113] valid_0's l1: 1.58488e+06 [114] valid_0's l1: 1.58443e+06 Early stopping, best iteration is: [84] valid_0's l1: 1.57931e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.92163e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.62051e+06 [3] valid_0's l1: 3.34817e+06 [4] valid_0's l1: 3.11253e+06 [5] valid_0's l1: 2.90409e+06 [6] valid_0's l1: 2.80635e+06 [7] valid_0's l1: 2.7208e+06 [8] valid_0's l1: 2.56756e+06 [9] valid_0's l1: 2.43616e+06 [10] valid_0's l1: 2.32151e+06 [11] valid_0's l1: 2.21898e+06 [12] valid_0's l1: 2.17242e+06 [13] valid_0's l1: 2.09115e+06 [14] valid_0's l1: 2.06811e+06 [15] valid_0's l1: 2.00714e+06 [16] valid_0's l1: 1.95139e+06 [17] valid_0's l1: 1.90248e+06 [18] valid_0's l1: 1.86218e+06 [19] valid_0's l1: 1.8302e+06 [20] valid_0's l1: 1.79563e+06 [21] valid_0's l1: 1.76865e+06 [22] valid_0's l1: 1.7457e+06 [23] valid_0's l1: 1.72433e+06 [24] valid_0's l1: 1.70488e+06 [25] valid_0's l1: 1.69042e+06 [26] valid_0's l1: 1.67479e+06 [27] valid_0's l1: 1.66188e+06 [28] valid_0's l1: 1.65166e+06 [29] valid_0's l1: 1.64814e+06 [30] valid_0's l1: 1.64016e+06 [31] valid_0's l1: 1.63678e+06 [32] valid_0's l1: 1.6258e+06 [33] valid_0's l1: 1.62234e+06 [34] valid_0's l1: 1.6162e+06 [35] valid_0's l1: 1.61559e+06 [36] valid_0's l1: 1.61593e+06 [37] valid_0's l1: 1.61618e+06 [38] valid_0's l1: 1.61257e+06 [39] valid_0's l1: 1.61192e+06 [40] valid_0's l1: 1.61066e+06 [41] valid_0's l1: 1.6056e+06 [42] valid_0's l1: 1.60543e+06 [43] valid_0's l1: 1.60025e+06 [44] valid_0's l1: 1.59933e+06 [45] valid_0's l1: 1.59983e+06 [46] valid_0's l1: 1.598e+06 [47] valid_0's l1: 1.5973e+06 [48] valid_0's l1: 1.59616e+06 [49] valid_0's l1: 1.59576e+06 [50] valid_0's l1: 1.59615e+06 [51] valid_0's l1: 1.59734e+06 [52] valid_0's l1: 1.59782e+06 [53] valid_0's l1: 1.59771e+06 [54] valid_0's l1: 1.60004e+06 [55] valid_0's l1: 1.59929e+06 [56] valid_0's l1: 1.59945e+06 [57] valid_0's l1: 1.59901e+06 [58] valid_0's l1: 1.59883e+06 [59] valid_0's l1: 1.59938e+06 [60] valid_0's l1: 1.5966e+06 [61] valid_0's l1: 1.59638e+06 [62] valid_0's l1: 1.59613e+06 [63] valid_0's l1: 1.5968e+06 [64] valid_0's l1: 1.59515e+06 [65] valid_0's l1: 1.59466e+06 [66] valid_0's l1: 1.59371e+06 [67] valid_0's l1: 1.59335e+06 [68] valid_0's l1: 1.59401e+06 [69] valid_0's l1: 1.59414e+06 [70] valid_0's l1: 1.59451e+06 [71] valid_0's l1: 1.59378e+06 [72] valid_0's l1: 1.59398e+06 [73] valid_0's l1: 1.59567e+06 [74] valid_0's l1: 1.59669e+06 [75] valid_0's l1: 1.59692e+06 [76] valid_0's l1: 1.59488e+06 [77] valid_0's l1: 1.59495e+06 [78] valid_0's l1: 1.59601e+06 [79] valid_0's l1: 1.59527e+06 [80] valid_0's l1: 1.59578e+06 [81] valid_0's l1: 1.59741e+06 [82] valid_0's l1: 1.59656e+06 [83] valid_0's l1: 1.59728e+06 [84] valid_0's l1: 1.59658e+06 [85] valid_0's l1: 1.59677e+06 [86] valid_0's l1: 1.59746e+06 [87] valid_0's l1: 1.59818e+06 [88] valid_0's l1: 1.59816e+06 [89] valid_0's l1: 1.59824e+06 [90] valid_0's l1: 1.5967e+06 [91] valid_0's l1: 1.59675e+06 [92] valid_0's l1: 1.59634e+06 [93] valid_0's l1: 1.59721e+06 [94] valid_0's l1: 1.59734e+06 [95] valid_0's l1: 1.59877e+06 [96] valid_0's l1: 1.59858e+06 [97] valid_0's l1: 1.59878e+06 Early stopping, best iteration is: [67] valid_0's l1: 1.59335e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.90081e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.59582e+06 [3] valid_0's l1: 3.33132e+06 [4] valid_0's l1: 3.098e+06 [5] valid_0's l1: 2.89114e+06 [6] valid_0's l1: 2.80556e+06 [7] valid_0's l1: 2.72592e+06 [8] valid_0's l1: 2.58085e+06 [9] valid_0's l1: 2.45021e+06 [10] valid_0's l1: 2.33527e+06 [11] valid_0's l1: 2.2391e+06 [12] valid_0's l1: 2.19693e+06 [13] valid_0's l1: 2.11329e+06 [14] valid_0's l1: 2.09066e+06 [15] valid_0's l1: 2.0198e+06 [16] valid_0's l1: 1.95972e+06 [17] valid_0's l1: 1.90723e+06 [18] valid_0's l1: 1.86494e+06 [19] valid_0's l1: 1.82292e+06 [20] valid_0's l1: 1.78873e+06 [21] valid_0's l1: 1.75883e+06 [22] valid_0's l1: 1.73505e+06 [23] valid_0's l1: 1.71207e+06 [24] valid_0's l1: 1.6975e+06 [25] valid_0's l1: 1.67955e+06 [26] valid_0's l1: 1.66097e+06 [27] valid_0's l1: 1.64628e+06 [28] valid_0's l1: 1.63646e+06 [29] valid_0's l1: 1.62795e+06 [30] valid_0's l1: 1.61504e+06 [31] valid_0's l1: 1.61319e+06 [32] valid_0's l1: 1.60404e+06 [33] valid_0's l1: 1.59766e+06 [34] valid_0's l1: 1.59666e+06 [35] valid_0's l1: 1.59503e+06 [36] valid_0's l1: 1.5944e+06 [37] valid_0's l1: 1.59336e+06 [38] valid_0's l1: 1.59277e+06 [39] valid_0's l1: 1.58696e+06 [40] valid_0's l1: 1.58668e+06 [41] valid_0's l1: 1.58367e+06 [42] valid_0's l1: 1.58363e+06 [43] valid_0's l1: 1.58274e+06 [44] valid_0's l1: 1.57959e+06 [45] valid_0's l1: 1.58042e+06 [46] valid_0's l1: 1.58166e+06 [47] valid_0's l1: 1.57879e+06 [48] valid_0's l1: 1.57911e+06 [49] valid_0's l1: 1.58157e+06 [50] valid_0's l1: 1.58284e+06 [51] valid_0's l1: 1.58312e+06 [52] valid_0's l1: 1.57803e+06 [53] valid_0's l1: 1.57922e+06 [54] valid_0's l1: 1.58002e+06 [55] valid_0's l1: 1.58115e+06 [56] valid_0's l1: 1.58287e+06 [57] valid_0's l1: 1.58232e+06 [58] valid_0's l1: 1.58297e+06 [59] valid_0's l1: 1.58295e+06 [60] valid_0's l1: 1.58504e+06 [61] valid_0's l1: 1.58515e+06 [62] valid_0's l1: 1.58561e+06 [63] valid_0's l1: 1.58709e+06 [64] valid_0's l1: 1.58507e+06 [65] valid_0's l1: 1.58536e+06 [66] valid_0's l1: 1.58432e+06 [67] valid_0's l1: 1.58613e+06 [68] valid_0's l1: 1.58538e+06 [69] valid_0's l1: 1.58021e+06 [70] valid_0's l1: 1.5804e+06 [71] valid_0's l1: 1.58138e+06 [72] valid_0's l1: 1.58121e+06 [73] valid_0's l1: 1.58254e+06 [74] valid_0's l1: 1.58233e+06 [75] valid_0's l1: 1.58032e+06 [76] valid_0's l1: 1.58063e+06 [77] valid_0's l1: 1.58148e+06 [78] valid_0's l1: 1.57924e+06 [79] valid_0's l1: 1.58047e+06 [80] valid_0's l1: 1.58135e+06 [81] valid_0's l1: 1.57956e+06 [82] valid_0's l1: 1.57943e+06 Early stopping, best iteration is: [52] valid_0's l1: 1.57803e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.84197e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.55342e+06 [3] valid_0's l1: 3.29726e+06 [4] valid_0's l1: 3.06367e+06 [5] valid_0's l1: 2.86201e+06 [6] valid_0's l1: 2.77263e+06 [7] valid_0's l1: 2.69511e+06 [8] valid_0's l1: 2.54647e+06 [9] valid_0's l1: 2.41522e+06 [10] valid_0's l1: 2.30494e+06 [11] valid_0's l1: 2.20889e+06 [12] valid_0's l1: 2.16019e+06 [13] valid_0's l1: 2.08103e+06 [14] valid_0's l1: 2.05287e+06 [15] valid_0's l1: 1.98986e+06 [16] valid_0's l1: 1.93642e+06 [17] valid_0's l1: 1.89289e+06 [18] valid_0's l1: 1.8552e+06 [19] valid_0's l1: 1.82202e+06 [20] valid_0's l1: 1.79212e+06 [21] valid_0's l1: 1.76743e+06 [22] valid_0's l1: 1.74428e+06 [23] valid_0's l1: 1.72953e+06 [24] valid_0's l1: 1.71457e+06 [25] valid_0's l1: 1.70052e+06 [26] valid_0's l1: 1.68713e+06 [27] valid_0's l1: 1.67176e+06 [28] valid_0's l1: 1.6637e+06 [29] valid_0's l1: 1.6565e+06 [30] valid_0's l1: 1.64877e+06 [31] valid_0's l1: 1.63661e+06 [32] valid_0's l1: 1.62954e+06 [33] valid_0's l1: 1.62726e+06 [34] valid_0's l1: 1.61977e+06 [35] valid_0's l1: 1.61731e+06 [36] valid_0's l1: 1.60926e+06 [37] valid_0's l1: 1.60815e+06 [38] valid_0's l1: 1.60559e+06 [39] valid_0's l1: 1.60279e+06 [40] valid_0's l1: 1.6027e+06 [41] valid_0's l1: 1.59799e+06 [42] valid_0's l1: 1.59741e+06 [43] valid_0's l1: 1.59672e+06 [44] valid_0's l1: 1.5961e+06 [45] valid_0's l1: 1.59397e+06 [46] valid_0's l1: 1.59023e+06 [47] valid_0's l1: 1.58949e+06 [48] valid_0's l1: 1.58952e+06 [49] valid_0's l1: 1.58677e+06 [50] valid_0's l1: 1.58555e+06 [51] valid_0's l1: 1.58606e+06 [52] valid_0's l1: 1.58418e+06 [53] valid_0's l1: 1.58507e+06 [54] valid_0's l1: 1.58497e+06 [55] valid_0's l1: 1.58581e+06 [56] valid_0's l1: 1.58421e+06 [57] valid_0's l1: 1.58225e+06 [58] valid_0's l1: 1.5824e+06 [59] valid_0's l1: 1.58158e+06 [60] valid_0's l1: 1.58005e+06 [61] valid_0's l1: 1.58137e+06 [62] valid_0's l1: 1.58155e+06 [63] valid_0's l1: 1.58091e+06 [64] valid_0's l1: 1.58157e+06 [65] valid_0's l1: 1.58289e+06 [66] valid_0's l1: 1.58426e+06 [67] valid_0's l1: 1.5831e+06 [68] valid_0's l1: 1.58122e+06 [69] valid_0's l1: 1.58157e+06 [70] valid_0's l1: 1.58213e+06 [71] valid_0's l1: 1.58303e+06 [72] valid_0's l1: 1.58277e+06 [73] valid_0's l1: 1.58444e+06 [74] valid_0's l1: 1.58325e+06 [75] valid_0's l1: 1.58386e+06 [76] valid_0's l1: 1.58379e+06 [77] valid_0's l1: 1.58402e+06 [78] valid_0's l1: 1.58249e+06 [79] valid_0's l1: 1.58142e+06 [80] valid_0's l1: 1.58035e+06 [81] valid_0's l1: 1.5809e+06 [82] valid_0's l1: 1.57963e+06 [83] valid_0's l1: 1.58035e+06 [84] valid_0's l1: 1.57931e+06 [85] valid_0's l1: 1.58015e+06 [86] valid_0's l1: 1.57969e+06 [87] valid_0's l1: 1.57984e+06 [88] valid_0's l1: 1.58188e+06 [89] valid_0's l1: 1.58304e+06 [90] valid_0's l1: 1.58135e+06 [91] valid_0's l1: 1.58232e+06 [92] valid_0's l1: 1.58257e+06 [93] valid_0's l1: 1.58241e+06 [94] valid_0's l1: 1.58202e+06 [95] valid_0's l1: 1.5829e+06 [96] valid_0's l1: 1.58211e+06 [97] valid_0's l1: 1.58216e+06 [98] valid_0's l1: 1.58011e+06 [99] valid_0's l1: 1.58153e+06 [100] valid_0's l1: 1.58087e+06 [101] valid_0's l1: 1.58134e+06 [102] valid_0's l1: 1.58232e+06 [103] valid_0's l1: 1.58281e+06 [104] valid_0's l1: 1.58261e+06 [105] valid_0's l1: 1.58244e+06 [106] valid_0's l1: 1.58099e+06 [107] valid_0's l1: 1.58132e+06 [108] valid_0's l1: 1.58327e+06 [109] valid_0's l1: 1.58179e+06 [110] valid_0's l1: 1.58224e+06 [111] valid_0's l1: 1.58337e+06 [112] valid_0's l1: 1.58372e+06 [113] valid_0's l1: 1.58488e+06 [114] valid_0's l1: 1.58443e+06 Early stopping, best iteration is: [84] valid_0's l1: 1.57931e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.92163e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.62051e+06 [3] valid_0's l1: 3.34817e+06 [4] valid_0's l1: 3.11253e+06 [5] valid_0's l1: 2.90409e+06 [6] valid_0's l1: 2.80635e+06 [7] valid_0's l1: 2.7208e+06 [8] valid_0's l1: 2.56756e+06 [9] valid_0's l1: 2.43616e+06 [10] valid_0's l1: 2.32151e+06 [11] valid_0's l1: 2.21898e+06 [12] valid_0's l1: 2.17242e+06 [13] valid_0's l1: 2.09115e+06 [14] valid_0's l1: 2.06811e+06 [15] valid_0's l1: 2.00714e+06 [16] valid_0's l1: 1.95139e+06 [17] valid_0's l1: 1.90248e+06 [18] valid_0's l1: 1.86218e+06 [19] valid_0's l1: 1.8302e+06 [20] valid_0's l1: 1.79563e+06 [21] valid_0's l1: 1.76865e+06 [22] valid_0's l1: 1.7457e+06 [23] valid_0's l1: 1.72433e+06 [24] valid_0's l1: 1.70488e+06 [25] valid_0's l1: 1.69042e+06 [26] valid_0's l1: 1.67479e+06 [27] valid_0's l1: 1.66188e+06 [28] valid_0's l1: 1.65166e+06 [29] valid_0's l1: 1.64814e+06 [30] valid_0's l1: 1.64016e+06 [31] valid_0's l1: 1.63678e+06 [32] valid_0's l1: 1.6258e+06 [33] valid_0's l1: 1.62234e+06 [34] valid_0's l1: 1.6162e+06 [35] valid_0's l1: 1.61559e+06 [36] valid_0's l1: 1.61593e+06 [37] valid_0's l1: 1.61618e+06 [38] valid_0's l1: 1.61257e+06 [39] valid_0's l1: 1.61192e+06 [40] valid_0's l1: 1.61066e+06 [41] valid_0's l1: 1.6056e+06 [42] valid_0's l1: 1.60543e+06 [43] valid_0's l1: 1.60025e+06 [44] valid_0's l1: 1.59933e+06 [45] valid_0's l1: 1.59983e+06 [46] valid_0's l1: 1.598e+06 [47] valid_0's l1: 1.5973e+06 [48] valid_0's l1: 1.59616e+06 [49] valid_0's l1: 1.59576e+06 [50] valid_0's l1: 1.59615e+06 [51] valid_0's l1: 1.59734e+06 [52] valid_0's l1: 1.59782e+06 [53] valid_0's l1: 1.59771e+06 [54] valid_0's l1: 1.60004e+06 [55] valid_0's l1: 1.59929e+06 [56] valid_0's l1: 1.59945e+06 [57] valid_0's l1: 1.59901e+06 [58] valid_0's l1: 1.59883e+06 [59] valid_0's l1: 1.59938e+06 [60] valid_0's l1: 1.5966e+06 [61] valid_0's l1: 1.59638e+06 [62] valid_0's l1: 1.59613e+06 [63] valid_0's l1: 1.5968e+06 [64] valid_0's l1: 1.59515e+06 [65] valid_0's l1: 1.59466e+06 [66] valid_0's l1: 1.59371e+06 [67] valid_0's l1: 1.59335e+06 [68] valid_0's l1: 1.59401e+06 [69] valid_0's l1: 1.59414e+06 [70] valid_0's l1: 1.59451e+06 [71] valid_0's l1: 1.59378e+06 [72] valid_0's l1: 1.59398e+06 [73] valid_0's l1: 1.59567e+06 [74] valid_0's l1: 1.59669e+06 [75] valid_0's l1: 1.59692e+06 [76] valid_0's l1: 1.59488e+06 [77] valid_0's l1: 1.59495e+06 [78] valid_0's l1: 1.59601e+06 [79] valid_0's l1: 1.59527e+06 [80] valid_0's l1: 1.59578e+06 [81] valid_0's l1: 1.59741e+06 [82] valid_0's l1: 1.59656e+06 [83] valid_0's l1: 1.59728e+06 [84] valid_0's l1: 1.59658e+06 [85] valid_0's l1: 1.59677e+06 [86] valid_0's l1: 1.59746e+06 [87] valid_0's l1: 1.59818e+06 [88] valid_0's l1: 1.59816e+06 [89] valid_0's l1: 1.59824e+06 [90] valid_0's l1: 1.5967e+06 [91] valid_0's l1: 1.59675e+06 [92] valid_0's l1: 1.59634e+06 [93] valid_0's l1: 1.59721e+06 [94] valid_0's l1: 1.59734e+06 [95] valid_0's l1: 1.59877e+06 [96] valid_0's l1: 1.59858e+06 [97] valid_0's l1: 1.59878e+06 Early stopping, best iteration is: [67] valid_0's l1: 1.59335e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.90081e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.59582e+06 [3] valid_0's l1: 3.33132e+06 [4] valid_0's l1: 3.098e+06 [5] valid_0's l1: 2.89114e+06 [6] valid_0's l1: 2.80556e+06 [7] valid_0's l1: 2.72592e+06 [8] valid_0's l1: 2.58085e+06 [9] valid_0's l1: 2.45021e+06 [10] valid_0's l1: 2.33527e+06 [11] valid_0's l1: 2.2391e+06 [12] valid_0's l1: 2.19693e+06 [13] valid_0's l1: 2.11329e+06 [14] valid_0's l1: 2.09066e+06 [15] valid_0's l1: 2.0198e+06 [16] valid_0's l1: 1.95972e+06 [17] valid_0's l1: 1.90723e+06 [18] valid_0's l1: 1.86494e+06 [19] valid_0's l1: 1.82292e+06 [20] valid_0's l1: 1.78873e+06 [21] valid_0's l1: 1.75883e+06 [22] valid_0's l1: 1.73505e+06 [23] valid_0's l1: 1.71207e+06 [24] valid_0's l1: 1.6975e+06 [25] valid_0's l1: 1.67955e+06 [26] valid_0's l1: 1.66097e+06 [27] valid_0's l1: 1.64628e+06 [28] valid_0's l1: 1.63646e+06 [29] valid_0's l1: 1.62795e+06 [30] valid_0's l1: 1.61504e+06 [31] valid_0's l1: 1.61319e+06 [32] valid_0's l1: 1.60404e+06 [33] valid_0's l1: 1.59766e+06 [34] valid_0's l1: 1.59666e+06 [35] valid_0's l1: 1.59503e+06 [36] valid_0's l1: 1.5944e+06 [37] valid_0's l1: 1.59336e+06 [38] valid_0's l1: 1.59277e+06 [39] valid_0's l1: 1.58696e+06 [40] valid_0's l1: 1.58668e+06 [41] valid_0's l1: 1.58367e+06 [42] valid_0's l1: 1.58363e+06 [43] valid_0's l1: 1.58274e+06 [44] valid_0's l1: 1.57959e+06 [45] valid_0's l1: 1.58042e+06 [46] valid_0's l1: 1.58166e+06 [47] valid_0's l1: 1.57879e+06 [48] valid_0's l1: 1.57911e+06 [49] valid_0's l1: 1.58157e+06 [50] valid_0's l1: 1.58284e+06 [51] valid_0's l1: 1.58312e+06 [52] valid_0's l1: 1.57803e+06 [53] valid_0's l1: 1.57922e+06 [54] valid_0's l1: 1.58002e+06 [55] valid_0's l1: 1.58115e+06 [56] valid_0's l1: 1.58287e+06 [57] valid_0's l1: 1.58232e+06 [58] valid_0's l1: 1.58297e+06 [59] valid_0's l1: 1.58295e+06 [60] valid_0's l1: 1.58504e+06 [61] valid_0's l1: 1.58515e+06 [62] valid_0's l1: 1.58561e+06 [63] valid_0's l1: 1.58709e+06 [64] valid_0's l1: 1.58507e+06 [65] valid_0's l1: 1.58536e+06 [66] valid_0's l1: 1.58432e+06 [67] valid_0's l1: 1.58613e+06 [68] valid_0's l1: 1.58538e+06 [69] valid_0's l1: 1.58021e+06 [70] valid_0's l1: 1.5804e+06 [71] valid_0's l1: 1.58138e+06 [72] valid_0's l1: 1.58121e+06 [73] valid_0's l1: 1.58254e+06 [74] valid_0's l1: 1.58233e+06 [75] valid_0's l1: 1.58032e+06 [76] valid_0's l1: 1.58063e+06 [77] valid_0's l1: 1.58148e+06 [78] valid_0's l1: 1.57924e+06 [79] valid_0's l1: 1.58047e+06 [80] valid_0's l1: 1.58135e+06 [81] valid_0's l1: 1.57956e+06 [82] valid_0's l1: 1.57943e+06 Early stopping, best iteration is: [52] valid_0's l1: 1.57803e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.84197e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.55342e+06 [3] valid_0's l1: 3.29726e+06 [4] valid_0's l1: 3.06367e+06 [5] valid_0's l1: 2.86201e+06 [6] valid_0's l1: 2.77263e+06 [7] valid_0's l1: 2.69511e+06 [8] valid_0's l1: 2.54647e+06 [9] valid_0's l1: 2.41522e+06 [10] valid_0's l1: 2.30494e+06 [11] valid_0's l1: 2.20889e+06 [12] valid_0's l1: 2.16019e+06 [13] valid_0's l1: 2.08103e+06 [14] valid_0's l1: 2.05287e+06 [15] valid_0's l1: 1.98986e+06 [16] valid_0's l1: 1.93642e+06 [17] valid_0's l1: 1.89289e+06 [18] valid_0's l1: 1.8552e+06 [19] valid_0's l1: 1.82202e+06 [20] valid_0's l1: 1.79212e+06 [21] valid_0's l1: 1.76743e+06 [22] valid_0's l1: 1.74428e+06 [23] valid_0's l1: 1.72953e+06 [24] valid_0's l1: 1.71457e+06 [25] valid_0's l1: 1.70052e+06 [26] valid_0's l1: 1.68713e+06 [27] valid_0's l1: 1.67176e+06 [28] valid_0's l1: 1.6637e+06 [29] valid_0's l1: 1.6565e+06 [30] valid_0's l1: 1.64877e+06 [31] valid_0's l1: 1.63661e+06 [32] valid_0's l1: 1.62954e+06 [33] valid_0's l1: 1.62726e+06 [34] valid_0's l1: 1.61977e+06 [35] valid_0's l1: 1.61731e+06 [36] valid_0's l1: 1.60926e+06 [37] valid_0's l1: 1.60815e+06 [38] valid_0's l1: 1.60559e+06 [39] valid_0's l1: 1.60279e+06 [40] valid_0's l1: 1.6027e+06 [41] valid_0's l1: 1.59799e+06 [42] valid_0's l1: 1.59741e+06 [43] valid_0's l1: 1.59672e+06 [44] valid_0's l1: 1.5961e+06 [45] valid_0's l1: 1.59397e+06 [46] valid_0's l1: 1.59023e+06 [47] valid_0's l1: 1.58949e+06 [48] valid_0's l1: 1.58952e+06 [49] valid_0's l1: 1.58677e+06 [50] valid_0's l1: 1.58555e+06 [51] valid_0's l1: 1.58606e+06 [52] valid_0's l1: 1.58418e+06 [53] valid_0's l1: 1.58507e+06 [54] valid_0's l1: 1.58497e+06 [55] valid_0's l1: 1.58581e+06 [56] valid_0's l1: 1.58421e+06 [57] valid_0's l1: 1.58225e+06 [58] valid_0's l1: 1.5824e+06 [59] valid_0's l1: 1.58158e+06 [60] valid_0's l1: 1.58005e+06 [61] valid_0's l1: 1.58137e+06 [62] valid_0's l1: 1.58155e+06 [63] valid_0's l1: 1.58091e+06 [64] valid_0's l1: 1.58157e+06 [65] valid_0's l1: 1.58289e+06 [66] valid_0's l1: 1.58426e+06 [67] valid_0's l1: 1.5831e+06 [68] valid_0's l1: 1.58122e+06 [69] valid_0's l1: 1.58157e+06 [70] valid_0's l1: 1.58213e+06 [71] valid_0's l1: 1.58303e+06 [72] valid_0's l1: 1.58277e+06 [73] valid_0's l1: 1.58444e+06 [74] valid_0's l1: 1.58325e+06 [75] valid_0's l1: 1.58386e+06 [76] valid_0's l1: 1.58379e+06 [77] valid_0's l1: 1.58402e+06 [78] valid_0's l1: 1.58249e+06 [79] valid_0's l1: 1.58142e+06 [80] valid_0's l1: 1.58035e+06 [81] valid_0's l1: 1.5809e+06 [82] valid_0's l1: 1.57963e+06 [83] valid_0's l1: 1.58035e+06 [84] valid_0's l1: 1.57931e+06 [85] valid_0's l1: 1.58015e+06 [86] valid_0's l1: 1.57969e+06 [87] valid_0's l1: 1.57984e+06 [88] valid_0's l1: 1.58188e+06 [89] valid_0's l1: 1.58304e+06 [90] valid_0's l1: 1.58135e+06 [91] valid_0's l1: 1.58232e+06 [92] valid_0's l1: 1.58257e+06 [93] valid_0's l1: 1.58241e+06 [94] valid_0's l1: 1.58202e+06 [95] valid_0's l1: 1.5829e+06 [96] valid_0's l1: 1.58211e+06 [97] valid_0's l1: 1.58216e+06 [98] valid_0's l1: 1.58011e+06 [99] valid_0's l1: 1.58153e+06 [100] valid_0's l1: 1.58087e+06 [101] valid_0's l1: 1.58134e+06 [102] valid_0's l1: 1.58232e+06 [103] valid_0's l1: 1.58281e+06 [104] valid_0's l1: 1.58261e+06 [105] valid_0's l1: 1.58244e+06 [106] valid_0's l1: 1.58099e+06 [107] valid_0's l1: 1.58132e+06 [108] valid_0's l1: 1.58327e+06 [109] valid_0's l1: 1.58179e+06 [110] valid_0's l1: 1.58224e+06 [111] valid_0's l1: 1.58337e+06 [112] valid_0's l1: 1.58372e+06 [113] valid_0's l1: 1.58488e+06 [114] valid_0's l1: 1.58443e+06 Early stopping, best iteration is: [84] valid_0's l1: 1.57931e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.92163e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.62051e+06 [3] valid_0's l1: 3.34817e+06 [4] valid_0's l1: 3.11253e+06 [5] valid_0's l1: 2.90409e+06 [6] valid_0's l1: 2.80635e+06 [7] valid_0's l1: 2.7208e+06 [8] valid_0's l1: 2.56756e+06 [9] valid_0's l1: 2.43616e+06 [10] valid_0's l1: 2.32151e+06 [11] valid_0's l1: 2.21898e+06 [12] valid_0's l1: 2.17242e+06 [13] valid_0's l1: 2.09115e+06 [14] valid_0's l1: 2.06811e+06 [15] valid_0's l1: 2.00714e+06 [16] valid_0's l1: 1.95139e+06 [17] valid_0's l1: 1.90248e+06 [18] valid_0's l1: 1.86218e+06 [19] valid_0's l1: 1.8302e+06 [20] valid_0's l1: 1.79563e+06 [21] valid_0's l1: 1.76865e+06 [22] valid_0's l1: 1.7457e+06 [23] valid_0's l1: 1.72433e+06 [24] valid_0's l1: 1.70488e+06 [25] valid_0's l1: 1.69042e+06 [26] valid_0's l1: 1.67479e+06 [27] valid_0's l1: 1.66188e+06 [28] valid_0's l1: 1.65166e+06 [29] valid_0's l1: 1.64814e+06 [30] valid_0's l1: 1.64016e+06 [31] valid_0's l1: 1.63678e+06 [32] valid_0's l1: 1.6258e+06 [33] valid_0's l1: 1.62234e+06 [34] valid_0's l1: 1.6162e+06 [35] valid_0's l1: 1.61559e+06 [36] valid_0's l1: 1.61593e+06 [37] valid_0's l1: 1.61618e+06 [38] valid_0's l1: 1.61257e+06 [39] valid_0's l1: 1.61192e+06 [40] valid_0's l1: 1.61066e+06 [41] valid_0's l1: 1.6056e+06 [42] valid_0's l1: 1.60543e+06 [43] valid_0's l1: 1.60025e+06 [44] valid_0's l1: 1.59933e+06 [45] valid_0's l1: 1.59983e+06 [46] valid_0's l1: 1.598e+06 [47] valid_0's l1: 1.5973e+06 [48] valid_0's l1: 1.59616e+06 [49] valid_0's l1: 1.59576e+06 [50] valid_0's l1: 1.59615e+06 [51] valid_0's l1: 1.59734e+06 [52] valid_0's l1: 1.59782e+06 [53] valid_0's l1: 1.59771e+06 [54] valid_0's l1: 1.60004e+06 [55] valid_0's l1: 1.59929e+06 [56] valid_0's l1: 1.59945e+06 [57] valid_0's l1: 1.59901e+06 [58] valid_0's l1: 1.59883e+06 [59] valid_0's l1: 1.59938e+06 [60] valid_0's l1: 1.5966e+06 [61] valid_0's l1: 1.59638e+06 [62] valid_0's l1: 1.59613e+06 [63] valid_0's l1: 1.5968e+06 [64] valid_0's l1: 1.59515e+06 [65] valid_0's l1: 1.59466e+06 [66] valid_0's l1: 1.59371e+06 [67] valid_0's l1: 1.59335e+06 [68] valid_0's l1: 1.59401e+06 [69] valid_0's l1: 1.59414e+06 [70] valid_0's l1: 1.59451e+06 [71] valid_0's l1: 1.59378e+06 [72] valid_0's l1: 1.59398e+06 [73] valid_0's l1: 1.59567e+06 [74] valid_0's l1: 1.59669e+06 [75] valid_0's l1: 1.59692e+06 [76] valid_0's l1: 1.59488e+06 [77] valid_0's l1: 1.59495e+06 [78] valid_0's l1: 1.59601e+06 [79] valid_0's l1: 1.59527e+06 [80] valid_0's l1: 1.59578e+06 [81] valid_0's l1: 1.59741e+06 [82] valid_0's l1: 1.59656e+06 [83] valid_0's l1: 1.59728e+06 [84] valid_0's l1: 1.59658e+06 [85] valid_0's l1: 1.59677e+06 [86] valid_0's l1: 1.59746e+06 [87] valid_0's l1: 1.59818e+06 [88] valid_0's l1: 1.59816e+06 [89] valid_0's l1: 1.59824e+06 [90] valid_0's l1: 1.5967e+06 [91] valid_0's l1: 1.59675e+06 [92] valid_0's l1: 1.59634e+06 [93] valid_0's l1: 1.59721e+06 [94] valid_0's l1: 1.59734e+06 [95] valid_0's l1: 1.59877e+06 [96] valid_0's l1: 1.59858e+06 [97] valid_0's l1: 1.59878e+06 Early stopping, best iteration is: [67] valid_0's l1: 1.59335e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.90081e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.59582e+06 [3] valid_0's l1: 3.33132e+06 [4] valid_0's l1: 3.098e+06 [5] valid_0's l1: 2.89114e+06 [6] valid_0's l1: 2.80556e+06 [7] valid_0's l1: 2.72592e+06 [8] valid_0's l1: 2.58085e+06 [9] valid_0's l1: 2.45021e+06 [10] valid_0's l1: 2.33527e+06 [11] valid_0's l1: 2.2391e+06 [12] valid_0's l1: 2.19693e+06 [13] valid_0's l1: 2.11329e+06 [14] valid_0's l1: 2.09066e+06 [15] valid_0's l1: 2.0198e+06 [16] valid_0's l1: 1.95972e+06 [17] valid_0's l1: 1.90723e+06 [18] valid_0's l1: 1.86494e+06 [19] valid_0's l1: 1.82292e+06 [20] valid_0's l1: 1.78873e+06 [21] valid_0's l1: 1.75883e+06 [22] valid_0's l1: 1.73505e+06 [23] valid_0's l1: 1.71207e+06 [24] valid_0's l1: 1.6975e+06 [25] valid_0's l1: 1.67955e+06 [26] valid_0's l1: 1.66097e+06 [27] valid_0's l1: 1.64628e+06 [28] valid_0's l1: 1.63646e+06 [29] valid_0's l1: 1.62795e+06 [30] valid_0's l1: 1.61504e+06 [31] valid_0's l1: 1.61319e+06 [32] valid_0's l1: 1.60404e+06 [33] valid_0's l1: 1.59766e+06 [34] valid_0's l1: 1.59666e+06 [35] valid_0's l1: 1.59503e+06 [36] valid_0's l1: 1.5944e+06 [37] valid_0's l1: 1.59336e+06 [38] valid_0's l1: 1.59277e+06 [39] valid_0's l1: 1.58696e+06 [40] valid_0's l1: 1.58668e+06 [41] valid_0's l1: 1.58367e+06 [42] valid_0's l1: 1.58363e+06 [43] valid_0's l1: 1.58274e+06 [44] valid_0's l1: 1.57959e+06 [45] valid_0's l1: 1.58042e+06 [46] valid_0's l1: 1.58166e+06 [47] valid_0's l1: 1.57879e+06 [48] valid_0's l1: 1.57911e+06 [49] valid_0's l1: 1.58157e+06 [50] valid_0's l1: 1.58284e+06 [51] valid_0's l1: 1.58312e+06 [52] valid_0's l1: 1.57803e+06 [53] valid_0's l1: 1.57922e+06 [54] valid_0's l1: 1.58002e+06 [55] valid_0's l1: 1.58115e+06 [56] valid_0's l1: 1.58287e+06 [57] valid_0's l1: 1.58232e+06 [58] valid_0's l1: 1.58297e+06 [59] valid_0's l1: 1.58295e+06 [60] valid_0's l1: 1.58504e+06 [61] valid_0's l1: 1.58515e+06 [62] valid_0's l1: 1.58561e+06 [63] valid_0's l1: 1.58709e+06 [64] valid_0's l1: 1.58507e+06 [65] valid_0's l1: 1.58536e+06 [66] valid_0's l1: 1.58432e+06 [67] valid_0's l1: 1.58613e+06 [68] valid_0's l1: 1.58538e+06 [69] valid_0's l1: 1.58021e+06 [70] valid_0's l1: 1.5804e+06 [71] valid_0's l1: 1.58138e+06 [72] valid_0's l1: 1.58121e+06 [73] valid_0's l1: 1.58254e+06 [74] valid_0's l1: 1.58233e+06 [75] valid_0's l1: 1.58032e+06 [76] valid_0's l1: 1.58063e+06 [77] valid_0's l1: 1.58148e+06 [78] valid_0's l1: 1.57924e+06 [79] valid_0's l1: 1.58047e+06 [80] valid_0's l1: 1.58135e+06 [81] valid_0's l1: 1.57956e+06 [82] valid_0's l1: 1.57943e+06 Early stopping, best iteration is: [52] valid_0's l1: 1.57803e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.84197e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.55342e+06 [3] valid_0's l1: 3.29726e+06 [4] valid_0's l1: 3.06367e+06 [5] valid_0's l1: 2.86201e+06 [6] valid_0's l1: 2.77263e+06 [7] valid_0's l1: 2.69511e+06 [8] valid_0's l1: 2.54647e+06 [9] valid_0's l1: 2.41522e+06 [10] valid_0's l1: 2.30494e+06 [11] valid_0's l1: 2.20889e+06 [12] valid_0's l1: 2.16019e+06 [13] valid_0's l1: 2.08103e+06 [14] valid_0's l1: 2.05287e+06 [15] valid_0's l1: 1.98986e+06 [16] valid_0's l1: 1.93642e+06 [17] valid_0's l1: 1.89289e+06 [18] valid_0's l1: 1.8552e+06 [19] valid_0's l1: 1.82202e+06 [20] valid_0's l1: 1.79212e+06 [21] valid_0's l1: 1.76743e+06 [22] valid_0's l1: 1.74428e+06 [23] valid_0's l1: 1.72953e+06 [24] valid_0's l1: 1.71457e+06 [25] valid_0's l1: 1.70052e+06 [26] valid_0's l1: 1.68713e+06 [27] valid_0's l1: 1.67176e+06 [28] valid_0's l1: 1.6637e+06 [29] valid_0's l1: 1.6565e+06 [30] valid_0's l1: 1.64877e+06 [31] valid_0's l1: 1.63661e+06 [32] valid_0's l1: 1.62954e+06 [33] valid_0's l1: 1.62726e+06 [34] valid_0's l1: 1.61977e+06 [35] valid_0's l1: 1.61731e+06 [36] valid_0's l1: 1.60926e+06 [37] valid_0's l1: 1.60815e+06 [38] valid_0's l1: 1.60559e+06 [39] valid_0's l1: 1.60279e+06 [40] valid_0's l1: 1.6027e+06 [41] valid_0's l1: 1.59799e+06 [42] valid_0's l1: 1.59741e+06 [43] valid_0's l1: 1.59672e+06 [44] valid_0's l1: 1.5961e+06 [45] valid_0's l1: 1.59397e+06 [46] valid_0's l1: 1.59023e+06 [47] valid_0's l1: 1.58949e+06 [48] valid_0's l1: 1.58952e+06 [49] valid_0's l1: 1.58677e+06 [50] valid_0's l1: 1.58555e+06 [51] valid_0's l1: 1.58606e+06 [52] valid_0's l1: 1.58418e+06 [53] valid_0's l1: 1.58507e+06 [54] valid_0's l1: 1.58497e+06 [55] valid_0's l1: 1.58581e+06 [56] valid_0's l1: 1.58421e+06 [57] valid_0's l1: 1.58225e+06 [58] valid_0's l1: 1.5824e+06 [59] valid_0's l1: 1.58158e+06 [60] valid_0's l1: 1.58005e+06 [61] valid_0's l1: 1.58137e+06 [62] valid_0's l1: 1.58155e+06 [63] valid_0's l1: 1.58091e+06 [64] valid_0's l1: 1.58157e+06 [65] valid_0's l1: 1.58289e+06 [66] valid_0's l1: 1.58426e+06 [67] valid_0's l1: 1.5831e+06 [68] valid_0's l1: 1.58122e+06 [69] valid_0's l1: 1.58157e+06 [70] valid_0's l1: 1.58213e+06 [71] valid_0's l1: 1.58303e+06 [72] valid_0's l1: 1.58277e+06 [73] valid_0's l1: 1.58444e+06 [74] valid_0's l1: 1.58325e+06 [75] valid_0's l1: 1.58386e+06 [76] valid_0's l1: 1.58379e+06 [77] valid_0's l1: 1.58402e+06 [78] valid_0's l1: 1.58249e+06 [79] valid_0's l1: 1.58142e+06 [80] valid_0's l1: 1.58035e+06 [81] valid_0's l1: 1.5809e+06 [82] valid_0's l1: 1.57963e+06 [83] valid_0's l1: 1.58035e+06 [84] valid_0's l1: 1.57931e+06 [85] valid_0's l1: 1.58015e+06 [86] valid_0's l1: 1.57969e+06 [87] valid_0's l1: 1.57984e+06 [88] valid_0's l1: 1.58188e+06 [89] valid_0's l1: 1.58304e+06 [90] valid_0's l1: 1.58135e+06 [91] valid_0's l1: 1.58232e+06 [92] valid_0's l1: 1.58257e+06 [93] valid_0's l1: 1.58241e+06 [94] valid_0's l1: 1.58202e+06 [95] valid_0's l1: 1.5829e+06 [96] valid_0's l1: 1.58211e+06 [97] valid_0's l1: 1.58216e+06 [98] valid_0's l1: 1.58011e+06 [99] valid_0's l1: 1.58153e+06 [100] valid_0's l1: 1.58087e+06 [101] valid_0's l1: 1.58134e+06 [102] valid_0's l1: 1.58232e+06 [103] valid_0's l1: 1.58281e+06 [104] valid_0's l1: 1.58261e+06 [105] valid_0's l1: 1.58244e+06 [106] valid_0's l1: 1.58099e+06 [107] valid_0's l1: 1.58132e+06 [108] valid_0's l1: 1.58327e+06 [109] valid_0's l1: 1.58179e+06 [110] valid_0's l1: 1.58224e+06 [111] valid_0's l1: 1.58337e+06 [112] valid_0's l1: 1.58372e+06 [113] valid_0's l1: 1.58488e+06 [114] valid_0's l1: 1.58443e+06 Early stopping, best iteration is: [84] valid_0's l1: 1.57931e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.92163e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.62051e+06 [3] valid_0's l1: 3.34817e+06 [4] valid_0's l1: 3.11253e+06 [5] valid_0's l1: 2.90409e+06 [6] valid_0's l1: 2.80635e+06 [7] valid_0's l1: 2.7208e+06 [8] valid_0's l1: 2.56756e+06 [9] valid_0's l1: 2.43616e+06 [10] valid_0's l1: 2.32151e+06 [11] valid_0's l1: 2.21898e+06 [12] valid_0's l1: 2.17242e+06 [13] valid_0's l1: 2.09115e+06 [14] valid_0's l1: 2.06811e+06 [15] valid_0's l1: 2.00714e+06 [16] valid_0's l1: 1.95139e+06 [17] valid_0's l1: 1.90248e+06 [18] valid_0's l1: 1.86218e+06 [19] valid_0's l1: 1.8302e+06 [20] valid_0's l1: 1.79563e+06 [21] valid_0's l1: 1.76865e+06 [22] valid_0's l1: 1.7457e+06 [23] valid_0's l1: 1.72433e+06 [24] valid_0's l1: 1.70488e+06 [25] valid_0's l1: 1.69042e+06 [26] valid_0's l1: 1.67479e+06 [27] valid_0's l1: 1.66188e+06 [28] valid_0's l1: 1.65166e+06 [29] valid_0's l1: 1.64814e+06 [30] valid_0's l1: 1.64016e+06 [31] valid_0's l1: 1.63678e+06 [32] valid_0's l1: 1.6258e+06 [33] valid_0's l1: 1.62234e+06 [34] valid_0's l1: 1.6162e+06 [35] valid_0's l1: 1.61559e+06 [36] valid_0's l1: 1.61593e+06 [37] valid_0's l1: 1.61618e+06 [38] valid_0's l1: 1.61257e+06 [39] valid_0's l1: 1.61192e+06 [40] valid_0's l1: 1.61066e+06 [41] valid_0's l1: 1.6056e+06 [42] valid_0's l1: 1.60543e+06 [43] valid_0's l1: 1.60025e+06 [44] valid_0's l1: 1.59933e+06 [45] valid_0's l1: 1.59983e+06 [46] valid_0's l1: 1.598e+06 [47] valid_0's l1: 1.5973e+06 [48] valid_0's l1: 1.59616e+06 [49] valid_0's l1: 1.59576e+06 [50] valid_0's l1: 1.59615e+06 [51] valid_0's l1: 1.59734e+06 [52] valid_0's l1: 1.59782e+06 [53] valid_0's l1: 1.59771e+06 [54] valid_0's l1: 1.60004e+06 [55] valid_0's l1: 1.59929e+06 [56] valid_0's l1: 1.59945e+06 [57] valid_0's l1: 1.59901e+06 [58] valid_0's l1: 1.59883e+06 [59] valid_0's l1: 1.59938e+06 [60] valid_0's l1: 1.5966e+06 [61] valid_0's l1: 1.59638e+06 [62] valid_0's l1: 1.59613e+06 [63] valid_0's l1: 1.5968e+06 [64] valid_0's l1: 1.59515e+06 [65] valid_0's l1: 1.59466e+06 [66] valid_0's l1: 1.59371e+06 [67] valid_0's l1: 1.59335e+06 [68] valid_0's l1: 1.59401e+06 [69] valid_0's l1: 1.59414e+06 [70] valid_0's l1: 1.59451e+06 [71] valid_0's l1: 1.59378e+06 [72] valid_0's l1: 1.59398e+06 [73] valid_0's l1: 1.59567e+06 [74] valid_0's l1: 1.59669e+06 [75] valid_0's l1: 1.59692e+06 [76] valid_0's l1: 1.59488e+06 [77] valid_0's l1: 1.59495e+06 [78] valid_0's l1: 1.59601e+06 [79] valid_0's l1: 1.59527e+06 [80] valid_0's l1: 1.59578e+06 [81] valid_0's l1: 1.59741e+06 [82] valid_0's l1: 1.59656e+06 [83] valid_0's l1: 1.59728e+06 [84] valid_0's l1: 1.59658e+06 [85] valid_0's l1: 1.59677e+06 [86] valid_0's l1: 1.59746e+06 [87] valid_0's l1: 1.59818e+06 [88] valid_0's l1: 1.59816e+06 [89] valid_0's l1: 1.59824e+06 [90] valid_0's l1: 1.5967e+06 [91] valid_0's l1: 1.59675e+06 [92] valid_0's l1: 1.59634e+06 [93] valid_0's l1: 1.59721e+06 [94] valid_0's l1: 1.59734e+06 [95] valid_0's l1: 1.59877e+06 [96] valid_0's l1: 1.59858e+06 [97] valid_0's l1: 1.59878e+06 Early stopping, best iteration is: [67] valid_0's l1: 1.59335e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.90081e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.59582e+06 [3] valid_0's l1: 3.33132e+06 [4] valid_0's l1: 3.098e+06 [5] valid_0's l1: 2.89114e+06 [6] valid_0's l1: 2.80556e+06 [7] valid_0's l1: 2.72592e+06 [8] valid_0's l1: 2.58085e+06 [9] valid_0's l1: 2.45021e+06 [10] valid_0's l1: 2.33527e+06 [11] valid_0's l1: 2.2391e+06 [12] valid_0's l1: 2.19693e+06 [13] valid_0's l1: 2.11329e+06 [14] valid_0's l1: 2.09066e+06 [15] valid_0's l1: 2.0198e+06 [16] valid_0's l1: 1.95972e+06 [17] valid_0's l1: 1.90723e+06 [18] valid_0's l1: 1.86494e+06 [19] valid_0's l1: 1.82292e+06 [20] valid_0's l1: 1.78873e+06 [21] valid_0's l1: 1.75883e+06 [22] valid_0's l1: 1.73505e+06 [23] valid_0's l1: 1.71207e+06 [24] valid_0's l1: 1.6975e+06 [25] valid_0's l1: 1.67955e+06 [26] valid_0's l1: 1.66097e+06 [27] valid_0's l1: 1.64628e+06 [28] valid_0's l1: 1.63646e+06 [29] valid_0's l1: 1.62795e+06 [30] valid_0's l1: 1.61504e+06 [31] valid_0's l1: 1.61319e+06 [32] valid_0's l1: 1.60404e+06 [33] valid_0's l1: 1.59766e+06 [34] valid_0's l1: 1.59666e+06 [35] valid_0's l1: 1.59503e+06 [36] valid_0's l1: 1.5944e+06 [37] valid_0's l1: 1.59336e+06 [38] valid_0's l1: 1.59277e+06 [39] valid_0's l1: 1.58696e+06 [40] valid_0's l1: 1.58668e+06 [41] valid_0's l1: 1.58367e+06 [42] valid_0's l1: 1.58363e+06 [43] valid_0's l1: 1.58274e+06 [44] valid_0's l1: 1.57959e+06 [45] valid_0's l1: 1.58042e+06 [46] valid_0's l1: 1.58166e+06 [47] valid_0's l1: 1.57879e+06 [48] valid_0's l1: 1.57911e+06 [49] valid_0's l1: 1.58157e+06 [50] valid_0's l1: 1.58284e+06 [51] valid_0's l1: 1.58312e+06 [52] valid_0's l1: 1.57803e+06 [53] valid_0's l1: 1.57922e+06 [54] valid_0's l1: 1.58002e+06 [55] valid_0's l1: 1.58115e+06 [56] valid_0's l1: 1.58287e+06 [57] valid_0's l1: 1.58232e+06 [58] valid_0's l1: 1.58297e+06 [59] valid_0's l1: 1.58295e+06 [60] valid_0's l1: 1.58504e+06 [61] valid_0's l1: 1.58515e+06 [62] valid_0's l1: 1.58561e+06 [63] valid_0's l1: 1.58709e+06 [64] valid_0's l1: 1.58507e+06 [65] valid_0's l1: 1.58536e+06 [66] valid_0's l1: 1.58432e+06 [67] valid_0's l1: 1.58613e+06 [68] valid_0's l1: 1.58538e+06 [69] valid_0's l1: 1.58021e+06 [70] valid_0's l1: 1.5804e+06 [71] valid_0's l1: 1.58138e+06 [72] valid_0's l1: 1.58121e+06 [73] valid_0's l1: 1.58254e+06 [74] valid_0's l1: 1.58233e+06 [75] valid_0's l1: 1.58032e+06 [76] valid_0's l1: 1.58063e+06 [77] valid_0's l1: 1.58148e+06 [78] valid_0's l1: 1.57924e+06 [79] valid_0's l1: 1.58047e+06 [80] valid_0's l1: 1.58135e+06 [81] valid_0's l1: 1.57956e+06 [82] valid_0's l1: 1.57943e+06 Early stopping, best iteration is: [52] valid_0's l1: 1.57803e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.84197e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.55342e+06 [3] valid_0's l1: 3.29726e+06 [4] valid_0's l1: 3.06367e+06 [5] valid_0's l1: 2.86201e+06 [6] valid_0's l1: 2.77263e+06 [7] valid_0's l1: 2.69511e+06 [8] valid_0's l1: 2.54647e+06 [9] valid_0's l1: 2.41522e+06 [10] valid_0's l1: 2.30494e+06 [11] valid_0's l1: 2.20889e+06 [12] valid_0's l1: 2.16019e+06 [13] valid_0's l1: 2.08103e+06 [14] valid_0's l1: 2.05287e+06 [15] valid_0's l1: 1.98986e+06 [16] valid_0's l1: 1.93642e+06 [17] valid_0's l1: 1.89289e+06 [18] valid_0's l1: 1.8552e+06 [19] valid_0's l1: 1.82202e+06 [20] valid_0's l1: 1.79212e+06 [21] valid_0's l1: 1.76743e+06 [22] valid_0's l1: 1.74428e+06 [23] valid_0's l1: 1.72953e+06 [24] valid_0's l1: 1.71457e+06 [25] valid_0's l1: 1.70052e+06 [26] valid_0's l1: 1.68713e+06 [27] valid_0's l1: 1.67176e+06 [28] valid_0's l1: 1.6637e+06 [29] valid_0's l1: 1.6565e+06 [30] valid_0's l1: 1.64877e+06 [31] valid_0's l1: 1.63661e+06 [32] valid_0's l1: 1.62954e+06 [33] valid_0's l1: 1.62726e+06 [34] valid_0's l1: 1.61977e+06 [35] valid_0's l1: 1.61731e+06 [36] valid_0's l1: 1.60926e+06 [37] valid_0's l1: 1.60815e+06 [38] valid_0's l1: 1.60559e+06 [39] valid_0's l1: 1.60279e+06 [40] valid_0's l1: 1.6027e+06 [41] valid_0's l1: 1.59799e+06 [42] valid_0's l1: 1.59741e+06 [43] valid_0's l1: 1.59672e+06 [44] valid_0's l1: 1.5961e+06 [45] valid_0's l1: 1.59397e+06 [46] valid_0's l1: 1.59023e+06 [47] valid_0's l1: 1.58949e+06 [48] valid_0's l1: 1.58952e+06 [49] valid_0's l1: 1.58677e+06 [50] valid_0's l1: 1.58555e+06 [51] valid_0's l1: 1.58606e+06 [52] valid_0's l1: 1.58418e+06 [53] valid_0's l1: 1.58507e+06 [54] valid_0's l1: 1.58497e+06 [55] valid_0's l1: 1.58581e+06 [56] valid_0's l1: 1.58421e+06 [57] valid_0's l1: 1.58225e+06 [58] valid_0's l1: 1.5824e+06 [59] valid_0's l1: 1.58158e+06 [60] valid_0's l1: 1.58005e+06 [61] valid_0's l1: 1.58137e+06 [62] valid_0's l1: 1.58155e+06 [63] valid_0's l1: 1.58091e+06 [64] valid_0's l1: 1.58157e+06 [65] valid_0's l1: 1.58289e+06 [66] valid_0's l1: 1.58426e+06 [67] valid_0's l1: 1.5831e+06 [68] valid_0's l1: 1.58122e+06 [69] valid_0's l1: 1.58157e+06 [70] valid_0's l1: 1.58213e+06 [71] valid_0's l1: 1.58303e+06 [72] valid_0's l1: 1.58277e+06 [73] valid_0's l1: 1.58444e+06 [74] valid_0's l1: 1.58325e+06 [75] valid_0's l1: 1.58386e+06 [76] valid_0's l1: 1.58379e+06 [77] valid_0's l1: 1.58402e+06 [78] valid_0's l1: 1.58249e+06 [79] valid_0's l1: 1.58142e+06 [80] valid_0's l1: 1.58035e+06 [81] valid_0's l1: 1.5809e+06 [82] valid_0's l1: 1.57963e+06 [83] valid_0's l1: 1.58035e+06 [84] valid_0's l1: 1.57931e+06 [85] valid_0's l1: 1.58015e+06 [86] valid_0's l1: 1.57969e+06 [87] valid_0's l1: 1.57984e+06 [88] valid_0's l1: 1.58188e+06 [89] valid_0's l1: 1.58304e+06 [90] valid_0's l1: 1.58135e+06 [91] valid_0's l1: 1.58232e+06 [92] valid_0's l1: 1.58257e+06 [93] valid_0's l1: 1.58241e+06 [94] valid_0's l1: 1.58202e+06 [95] valid_0's l1: 1.5829e+06 [96] valid_0's l1: 1.58211e+06 [97] valid_0's l1: 1.58216e+06 [98] valid_0's l1: 1.58011e+06 [99] valid_0's l1: 1.58153e+06 [100] valid_0's l1: 1.58087e+06 [101] valid_0's l1: 1.58134e+06 [102] valid_0's l1: 1.58232e+06 [103] valid_0's l1: 1.58281e+06 [104] valid_0's l1: 1.58261e+06 [105] valid_0's l1: 1.58244e+06 [106] valid_0's l1: 1.58099e+06 [107] valid_0's l1: 1.58132e+06 [108] valid_0's l1: 1.58327e+06 [109] valid_0's l1: 1.58179e+06 [110] valid_0's l1: 1.58224e+06 [111] valid_0's l1: 1.58337e+06 [112] valid_0's l1: 1.58372e+06 [113] valid_0's l1: 1.58488e+06 [114] valid_0's l1: 1.58443e+06 Early stopping, best iteration is: [84] valid_0's l1: 1.57931e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.92163e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.62051e+06 [3] valid_0's l1: 3.34817e+06 [4] valid_0's l1: 3.11253e+06 [5] valid_0's l1: 2.90409e+06 [6] valid_0's l1: 2.80635e+06 [7] valid_0's l1: 2.7208e+06 [8] valid_0's l1: 2.56756e+06 [9] valid_0's l1: 2.43616e+06 [10] valid_0's l1: 2.32151e+06 [11] valid_0's l1: 2.21898e+06 [12] valid_0's l1: 2.17242e+06 [13] valid_0's l1: 2.09115e+06 [14] valid_0's l1: 2.06811e+06 [15] valid_0's l1: 2.00714e+06 [16] valid_0's l1: 1.95139e+06 [17] valid_0's l1: 1.90248e+06 [18] valid_0's l1: 1.86218e+06 [19] valid_0's l1: 1.8302e+06 [20] valid_0's l1: 1.79563e+06 [21] valid_0's l1: 1.76865e+06 [22] valid_0's l1: 1.7457e+06 [23] valid_0's l1: 1.72433e+06 [24] valid_0's l1: 1.70488e+06 [25] valid_0's l1: 1.69042e+06 [26] valid_0's l1: 1.67479e+06 [27] valid_0's l1: 1.66188e+06 [28] valid_0's l1: 1.65166e+06 [29] valid_0's l1: 1.64814e+06 [30] valid_0's l1: 1.64016e+06 [31] valid_0's l1: 1.63678e+06 [32] valid_0's l1: 1.6258e+06 [33] valid_0's l1: 1.62234e+06 [34] valid_0's l1: 1.6162e+06 [35] valid_0's l1: 1.61559e+06 [36] valid_0's l1: 1.61593e+06 [37] valid_0's l1: 1.61618e+06 [38] valid_0's l1: 1.61257e+06 [39] valid_0's l1: 1.61192e+06 [40] valid_0's l1: 1.61066e+06 [41] valid_0's l1: 1.6056e+06 [42] valid_0's l1: 1.60543e+06 [43] valid_0's l1: 1.60025e+06 [44] valid_0's l1: 1.59933e+06 [45] valid_0's l1: 1.59983e+06 [46] valid_0's l1: 1.598e+06 [47] valid_0's l1: 1.5973e+06 [48] valid_0's l1: 1.59616e+06 [49] valid_0's l1: 1.59576e+06 [50] valid_0's l1: 1.59615e+06 [51] valid_0's l1: 1.59734e+06 [52] valid_0's l1: 1.59782e+06 [53] valid_0's l1: 1.59771e+06 [54] valid_0's l1: 1.60004e+06 [55] valid_0's l1: 1.59929e+06 [56] valid_0's l1: 1.59945e+06 [57] valid_0's l1: 1.59901e+06 [58] valid_0's l1: 1.59883e+06 [59] valid_0's l1: 1.59938e+06 [60] valid_0's l1: 1.5966e+06 [61] valid_0's l1: 1.59638e+06 [62] valid_0's l1: 1.59613e+06 [63] valid_0's l1: 1.5968e+06 [64] valid_0's l1: 1.59515e+06 [65] valid_0's l1: 1.59466e+06 [66] valid_0's l1: 1.59371e+06 [67] valid_0's l1: 1.59335e+06 [68] valid_0's l1: 1.59401e+06 [69] valid_0's l1: 1.59414e+06 [70] valid_0's l1: 1.59451e+06 [71] valid_0's l1: 1.59378e+06 [72] valid_0's l1: 1.59398e+06 [73] valid_0's l1: 1.59567e+06 [74] valid_0's l1: 1.59669e+06 [75] valid_0's l1: 1.59692e+06 [76] valid_0's l1: 1.59488e+06 [77] valid_0's l1: 1.59495e+06 [78] valid_0's l1: 1.59601e+06 [79] valid_0's l1: 1.59527e+06 [80] valid_0's l1: 1.59578e+06 [81] valid_0's l1: 1.59741e+06 [82] valid_0's l1: 1.59656e+06 [83] valid_0's l1: 1.59728e+06 [84] valid_0's l1: 1.59658e+06 [85] valid_0's l1: 1.59677e+06 [86] valid_0's l1: 1.59746e+06 [87] valid_0's l1: 1.59818e+06 [88] valid_0's l1: 1.59816e+06 [89] valid_0's l1: 1.59824e+06 [90] valid_0's l1: 1.5967e+06 [91] valid_0's l1: 1.59675e+06 [92] valid_0's l1: 1.59634e+06 [93] valid_0's l1: 1.59721e+06 [94] valid_0's l1: 1.59734e+06 [95] valid_0's l1: 1.59877e+06 [96] valid_0's l1: 1.59858e+06 [97] valid_0's l1: 1.59878e+06 Early stopping, best iteration is: [67] valid_0's l1: 1.59335e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.90081e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.59582e+06 [3] valid_0's l1: 3.33132e+06 [4] valid_0's l1: 3.098e+06 [5] valid_0's l1: 2.89114e+06 [6] valid_0's l1: 2.80556e+06 [7] valid_0's l1: 2.72592e+06 [8] valid_0's l1: 2.58085e+06 [9] valid_0's l1: 2.45021e+06 [10] valid_0's l1: 2.33527e+06 [11] valid_0's l1: 2.2391e+06 [12] valid_0's l1: 2.19693e+06 [13] valid_0's l1: 2.11329e+06 [14] valid_0's l1: 2.09066e+06 [15] valid_0's l1: 2.0198e+06 [16] valid_0's l1: 1.95972e+06 [17] valid_0's l1: 1.90723e+06 [18] valid_0's l1: 1.86494e+06 [19] valid_0's l1: 1.82292e+06 [20] valid_0's l1: 1.78873e+06 [21] valid_0's l1: 1.75883e+06 [22] valid_0's l1: 1.73505e+06 [23] valid_0's l1: 1.71207e+06 [24] valid_0's l1: 1.6975e+06 [25] valid_0's l1: 1.67955e+06 [26] valid_0's l1: 1.66097e+06 [27] valid_0's l1: 1.64628e+06 [28] valid_0's l1: 1.63646e+06 [29] valid_0's l1: 1.62795e+06 [30] valid_0's l1: 1.61504e+06 [31] valid_0's l1: 1.61319e+06 [32] valid_0's l1: 1.60404e+06 [33] valid_0's l1: 1.59766e+06 [34] valid_0's l1: 1.59666e+06 [35] valid_0's l1: 1.59503e+06 [36] valid_0's l1: 1.5944e+06 [37] valid_0's l1: 1.59336e+06 [38] valid_0's l1: 1.59277e+06 [39] valid_0's l1: 1.58696e+06 [40] valid_0's l1: 1.58668e+06 [41] valid_0's l1: 1.58367e+06 [42] valid_0's l1: 1.58363e+06 [43] valid_0's l1: 1.58274e+06 [44] valid_0's l1: 1.57959e+06 [45] valid_0's l1: 1.58042e+06 [46] valid_0's l1: 1.58166e+06 [47] valid_0's l1: 1.57879e+06 [48] valid_0's l1: 1.57911e+06 [49] valid_0's l1: 1.58157e+06 [50] valid_0's l1: 1.58284e+06 [51] valid_0's l1: 1.58312e+06 [52] valid_0's l1: 1.57803e+06 [53] valid_0's l1: 1.57922e+06 [54] valid_0's l1: 1.58002e+06 [55] valid_0's l1: 1.58115e+06 [56] valid_0's l1: 1.58287e+06 [57] valid_0's l1: 1.58232e+06 [58] valid_0's l1: 1.58297e+06 [59] valid_0's l1: 1.58295e+06 [60] valid_0's l1: 1.58504e+06 [61] valid_0's l1: 1.58515e+06 [62] valid_0's l1: 1.58561e+06 [63] valid_0's l1: 1.58709e+06 [64] valid_0's l1: 1.58507e+06 [65] valid_0's l1: 1.58536e+06 [66] valid_0's l1: 1.58432e+06 [67] valid_0's l1: 1.58613e+06 [68] valid_0's l1: 1.58538e+06 [69] valid_0's l1: 1.58021e+06 [70] valid_0's l1: 1.5804e+06 [71] valid_0's l1: 1.58138e+06 [72] valid_0's l1: 1.58121e+06 [73] valid_0's l1: 1.58254e+06 [74] valid_0's l1: 1.58233e+06 [75] valid_0's l1: 1.58032e+06 [76] valid_0's l1: 1.58063e+06 [77] valid_0's l1: 1.58148e+06 [78] valid_0's l1: 1.57924e+06 [79] valid_0's l1: 1.58047e+06 [80] valid_0's l1: 1.58135e+06 [81] valid_0's l1: 1.57956e+06 [82] valid_0's l1: 1.57943e+06 Early stopping, best iteration is: [52] valid_0's l1: 1.57803e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.84197e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.55342e+06 [3] valid_0's l1: 3.29726e+06 [4] valid_0's l1: 3.06367e+06 [5] valid_0's l1: 2.86201e+06 [6] valid_0's l1: 2.77263e+06 [7] valid_0's l1: 2.69511e+06 [8] valid_0's l1: 2.54647e+06 [9] valid_0's l1: 2.41522e+06 [10] valid_0's l1: 2.30494e+06 [11] valid_0's l1: 2.20889e+06 [12] valid_0's l1: 2.16019e+06 [13] valid_0's l1: 2.08103e+06 [14] valid_0's l1: 2.05287e+06 [15] valid_0's l1: 1.98986e+06 [16] valid_0's l1: 1.93642e+06 [17] valid_0's l1: 1.89289e+06 [18] valid_0's l1: 1.8552e+06 [19] valid_0's l1: 1.82202e+06 [20] valid_0's l1: 1.79212e+06 [21] valid_0's l1: 1.76743e+06 [22] valid_0's l1: 1.74428e+06 [23] valid_0's l1: 1.72953e+06 [24] valid_0's l1: 1.71457e+06 [25] valid_0's l1: 1.70052e+06 [26] valid_0's l1: 1.68713e+06 [27] valid_0's l1: 1.67176e+06 [28] valid_0's l1: 1.6637e+06 [29] valid_0's l1: 1.6565e+06 [30] valid_0's l1: 1.64877e+06 [31] valid_0's l1: 1.63661e+06 [32] valid_0's l1: 1.62954e+06 [33] valid_0's l1: 1.62726e+06 [34] valid_0's l1: 1.61977e+06 [35] valid_0's l1: 1.61731e+06 [36] valid_0's l1: 1.60926e+06 [37] valid_0's l1: 1.60815e+06 [38] valid_0's l1: 1.60559e+06 [39] valid_0's l1: 1.60279e+06 [40] valid_0's l1: 1.6027e+06 [41] valid_0's l1: 1.59799e+06 [42] valid_0's l1: 1.59741e+06 [43] valid_0's l1: 1.59672e+06 [44] valid_0's l1: 1.5961e+06 [45] valid_0's l1: 1.59397e+06 [46] valid_0's l1: 1.59023e+06 [47] valid_0's l1: 1.58949e+06 [48] valid_0's l1: 1.58952e+06 [49] valid_0's l1: 1.58677e+06 [50] valid_0's l1: 1.58555e+06 [51] valid_0's l1: 1.58606e+06 [52] valid_0's l1: 1.58418e+06 [53] valid_0's l1: 1.58507e+06 [54] valid_0's l1: 1.58497e+06 [55] valid_0's l1: 1.58581e+06 [56] valid_0's l1: 1.58421e+06 [57] valid_0's l1: 1.58225e+06 [58] valid_0's l1: 1.5824e+06 [59] valid_0's l1: 1.58158e+06 [60] valid_0's l1: 1.58005e+06 [61] valid_0's l1: 1.58137e+06 [62] valid_0's l1: 1.58155e+06 [63] valid_0's l1: 1.58091e+06 [64] valid_0's l1: 1.58157e+06 [65] valid_0's l1: 1.58289e+06 [66] valid_0's l1: 1.58426e+06 [67] valid_0's l1: 1.5831e+06 [68] valid_0's l1: 1.58122e+06 [69] valid_0's l1: 1.58157e+06 [70] valid_0's l1: 1.58213e+06 [71] valid_0's l1: 1.58303e+06 [72] valid_0's l1: 1.58277e+06 [73] valid_0's l1: 1.58444e+06 [74] valid_0's l1: 1.58325e+06 [75] valid_0's l1: 1.58386e+06 [76] valid_0's l1: 1.58379e+06 [77] valid_0's l1: 1.58402e+06 [78] valid_0's l1: 1.58249e+06 [79] valid_0's l1: 1.58142e+06 [80] valid_0's l1: 1.58035e+06 [81] valid_0's l1: 1.5809e+06 [82] valid_0's l1: 1.57963e+06 [83] valid_0's l1: 1.58035e+06 [84] valid_0's l1: 1.57931e+06 [85] valid_0's l1: 1.58015e+06 [86] valid_0's l1: 1.57969e+06 [87] valid_0's l1: 1.57984e+06 [88] valid_0's l1: 1.58188e+06 [89] valid_0's l1: 1.58304e+06 [90] valid_0's l1: 1.58135e+06 [91] valid_0's l1: 1.58232e+06 [92] valid_0's l1: 1.58257e+06 [93] valid_0's l1: 1.58241e+06 [94] valid_0's l1: 1.58202e+06 [95] valid_0's l1: 1.5829e+06 [96] valid_0's l1: 1.58211e+06 [97] valid_0's l1: 1.58216e+06 [98] valid_0's l1: 1.58011e+06 [99] valid_0's l1: 1.58153e+06 [100] valid_0's l1: 1.58087e+06 [101] valid_0's l1: 1.58134e+06 [102] valid_0's l1: 1.58232e+06 [103] valid_0's l1: 1.58281e+06 [104] valid_0's l1: 1.58261e+06 [105] valid_0's l1: 1.58244e+06 [106] valid_0's l1: 1.58099e+06 [107] valid_0's l1: 1.58132e+06 [108] valid_0's l1: 1.58327e+06 [109] valid_0's l1: 1.58179e+06 [110] valid_0's l1: 1.58224e+06 [111] valid_0's l1: 1.58337e+06 [112] valid_0's l1: 1.58372e+06 [113] valid_0's l1: 1.58488e+06 [114] valid_0's l1: 1.58443e+06 Early stopping, best iteration is: [84] valid_0's l1: 1.57931e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.92163e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.62051e+06 [3] valid_0's l1: 3.34817e+06 [4] valid_0's l1: 3.11253e+06 [5] valid_0's l1: 2.90409e+06 [6] valid_0's l1: 2.80635e+06 [7] valid_0's l1: 2.7208e+06 [8] valid_0's l1: 2.56756e+06 [9] valid_0's l1: 2.43616e+06 [10] valid_0's l1: 2.32151e+06 [11] valid_0's l1: 2.21898e+06 [12] valid_0's l1: 2.17242e+06 [13] valid_0's l1: 2.09115e+06 [14] valid_0's l1: 2.06811e+06 [15] valid_0's l1: 2.00714e+06 [16] valid_0's l1: 1.95139e+06 [17] valid_0's l1: 1.90248e+06 [18] valid_0's l1: 1.86218e+06 [19] valid_0's l1: 1.8302e+06 [20] valid_0's l1: 1.79563e+06 [21] valid_0's l1: 1.76865e+06 [22] valid_0's l1: 1.7457e+06 [23] valid_0's l1: 1.72433e+06 [24] valid_0's l1: 1.70488e+06 [25] valid_0's l1: 1.69042e+06 [26] valid_0's l1: 1.67479e+06 [27] valid_0's l1: 1.66188e+06 [28] valid_0's l1: 1.65166e+06 [29] valid_0's l1: 1.64814e+06 [30] valid_0's l1: 1.64016e+06 [31] valid_0's l1: 1.63678e+06 [32] valid_0's l1: 1.6258e+06 [33] valid_0's l1: 1.62234e+06 [34] valid_0's l1: 1.6162e+06 [35] valid_0's l1: 1.61559e+06 [36] valid_0's l1: 1.61593e+06 [37] valid_0's l1: 1.61618e+06 [38] valid_0's l1: 1.61257e+06 [39] valid_0's l1: 1.61192e+06 [40] valid_0's l1: 1.61066e+06 [41] valid_0's l1: 1.6056e+06 [42] valid_0's l1: 1.60543e+06 [43] valid_0's l1: 1.60025e+06 [44] valid_0's l1: 1.59933e+06 [45] valid_0's l1: 1.59983e+06 [46] valid_0's l1: 1.598e+06 [47] valid_0's l1: 1.5973e+06 [48] valid_0's l1: 1.59616e+06 [49] valid_0's l1: 1.59576e+06 [50] valid_0's l1: 1.59615e+06 [51] valid_0's l1: 1.59734e+06 [52] valid_0's l1: 1.59782e+06 [53] valid_0's l1: 1.59771e+06 [54] valid_0's l1: 1.60004e+06 [55] valid_0's l1: 1.59929e+06 [56] valid_0's l1: 1.59945e+06 [57] valid_0's l1: 1.59901e+06 [58] valid_0's l1: 1.59883e+06 [59] valid_0's l1: 1.59938e+06 [60] valid_0's l1: 1.5966e+06 [61] valid_0's l1: 1.59638e+06 [62] valid_0's l1: 1.59613e+06 [63] valid_0's l1: 1.5968e+06 [64] valid_0's l1: 1.59515e+06 [65] valid_0's l1: 1.59466e+06 [66] valid_0's l1: 1.59371e+06 [67] valid_0's l1: 1.59335e+06 [68] valid_0's l1: 1.59401e+06 [69] valid_0's l1: 1.59414e+06 [70] valid_0's l1: 1.59451e+06 [71] valid_0's l1: 1.59378e+06 [72] valid_0's l1: 1.59398e+06 [73] valid_0's l1: 1.59567e+06 [74] valid_0's l1: 1.59669e+06 [75] valid_0's l1: 1.59692e+06 [76] valid_0's l1: 1.59488e+06 [77] valid_0's l1: 1.59495e+06 [78] valid_0's l1: 1.59601e+06 [79] valid_0's l1: 1.59527e+06 [80] valid_0's l1: 1.59578e+06 [81] valid_0's l1: 1.59741e+06 [82] valid_0's l1: 1.59656e+06 [83] valid_0's l1: 1.59728e+06 [84] valid_0's l1: 1.59658e+06 [85] valid_0's l1: 1.59677e+06 [86] valid_0's l1: 1.59746e+06 [87] valid_0's l1: 1.59818e+06 [88] valid_0's l1: 1.59816e+06 [89] valid_0's l1: 1.59824e+06 [90] valid_0's l1: 1.5967e+06 [91] valid_0's l1: 1.59675e+06 [92] valid_0's l1: 1.59634e+06 [93] valid_0's l1: 1.59721e+06 [94] valid_0's l1: 1.59734e+06 [95] valid_0's l1: 1.59877e+06 [96] valid_0's l1: 1.59858e+06 [97] valid_0's l1: 1.59878e+06 Early stopping, best iteration is: [67] valid_0's l1: 1.59335e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.893e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.58967e+06 [3] valid_0's l1: 3.32468e+06 [4] valid_0's l1: 3.09825e+06 [5] valid_0's l1: 2.89071e+06 [6] valid_0's l1: 2.80154e+06 [7] valid_0's l1: 2.71644e+06 [8] valid_0's l1: 2.56741e+06 [9] valid_0's l1: 2.43756e+06 [10] valid_0's l1: 2.32643e+06 [11] valid_0's l1: 2.23079e+06 [12] valid_0's l1: 2.18948e+06 [13] valid_0's l1: 2.10749e+06 [14] valid_0's l1: 2.08242e+06 [15] valid_0's l1: 2.01493e+06 [16] valid_0's l1: 1.95632e+06 [17] valid_0's l1: 1.90485e+06 [18] valid_0's l1: 1.86042e+06 [19] valid_0's l1: 1.82396e+06 [20] valid_0's l1: 1.79462e+06 [21] valid_0's l1: 1.76725e+06 [22] valid_0's l1: 1.7403e+06 [23] valid_0's l1: 1.71626e+06 [24] valid_0's l1: 1.69631e+06 [25] valid_0's l1: 1.67999e+06 [26] valid_0's l1: 1.66406e+06 [27] valid_0's l1: 1.65339e+06 [28] valid_0's l1: 1.64006e+06 [29] valid_0's l1: 1.63299e+06 [30] valid_0's l1: 1.63076e+06 [31] valid_0's l1: 1.6258e+06 [32] valid_0's l1: 1.62122e+06 [33] valid_0's l1: 1.616e+06 [34] valid_0's l1: 1.61383e+06 [35] valid_0's l1: 1.61383e+06 [36] valid_0's l1: 1.60584e+06 [37] valid_0's l1: 1.60486e+06 [38] valid_0's l1: 1.60448e+06 [39] valid_0's l1: 1.59697e+06 [40] valid_0's l1: 1.5953e+06 [41] valid_0's l1: 1.59587e+06 [42] valid_0's l1: 1.59189e+06 [43] valid_0's l1: 1.59228e+06 [44] valid_0's l1: 1.58958e+06 [45] valid_0's l1: 1.58835e+06 [46] valid_0's l1: 1.5905e+06 [47] valid_0's l1: 1.59163e+06 [48] valid_0's l1: 1.58945e+06 [49] valid_0's l1: 1.59093e+06 [50] valid_0's l1: 1.58741e+06 [51] valid_0's l1: 1.58842e+06 [52] valid_0's l1: 1.58895e+06 [53] valid_0's l1: 1.59036e+06 [54] valid_0's l1: 1.59301e+06 [55] valid_0's l1: 1.59183e+06 [56] valid_0's l1: 1.58957e+06 [57] valid_0's l1: 1.59206e+06 [58] valid_0's l1: 1.5917e+06 [59] valid_0's l1: 1.59371e+06 [60] valid_0's l1: 1.59457e+06 [61] valid_0's l1: 1.59512e+06 [62] valid_0's l1: 1.59525e+06 [63] valid_0's l1: 1.59274e+06 [64] valid_0's l1: 1.5912e+06 [65] valid_0's l1: 1.59183e+06 [66] valid_0's l1: 1.59296e+06 [67] valid_0's l1: 1.5923e+06 [68] valid_0's l1: 1.59439e+06 [69] valid_0's l1: 1.59513e+06 [70] valid_0's l1: 1.59526e+06 [71] valid_0's l1: 1.59688e+06 [72] valid_0's l1: 1.59705e+06 [73] valid_0's l1: 1.59793e+06 [74] valid_0's l1: 1.59769e+06 [75] valid_0's l1: 1.59656e+06 [76] valid_0's l1: 1.59516e+06 [77] valid_0's l1: 1.59735e+06 [78] valid_0's l1: 1.59631e+06 [79] valid_0's l1: 1.5967e+06 [80] valid_0's l1: 1.59598e+06 Early stopping, best iteration is: [50] valid_0's l1: 1.58741e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.83364e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.5461e+06 [3] valid_0's l1: 3.29059e+06 [4] valid_0's l1: 3.05995e+06 [5] valid_0's l1: 2.86177e+06 [6] valid_0's l1: 2.77283e+06 [7] valid_0's l1: 2.69017e+06 [8] valid_0's l1: 2.54159e+06 [9] valid_0's l1: 2.41143e+06 [10] valid_0's l1: 2.30017e+06 [11] valid_0's l1: 2.20183e+06 [12] valid_0's l1: 2.15705e+06 [13] valid_0's l1: 2.07987e+06 [14] valid_0's l1: 2.05065e+06 [15] valid_0's l1: 1.98821e+06 [16] valid_0's l1: 1.93406e+06 [17] valid_0's l1: 1.88841e+06 [18] valid_0's l1: 1.8524e+06 [19] valid_0's l1: 1.81851e+06 [20] valid_0's l1: 1.78965e+06 [21] valid_0's l1: 1.76343e+06 [22] valid_0's l1: 1.74308e+06 [23] valid_0's l1: 1.7241e+06 [24] valid_0's l1: 1.70645e+06 [25] valid_0's l1: 1.69239e+06 [26] valid_0's l1: 1.67826e+06 [27] valid_0's l1: 1.66647e+06 [28] valid_0's l1: 1.65689e+06 [29] valid_0's l1: 1.64698e+06 [30] valid_0's l1: 1.63783e+06 [31] valid_0's l1: 1.62995e+06 [32] valid_0's l1: 1.62507e+06 [33] valid_0's l1: 1.62243e+06 [34] valid_0's l1: 1.61622e+06 [35] valid_0's l1: 1.61111e+06 [36] valid_0's l1: 1.6069e+06 [37] valid_0's l1: 1.6063e+06 [38] valid_0's l1: 1.60097e+06 [39] valid_0's l1: 1.60005e+06 [40] valid_0's l1: 1.59611e+06 [41] valid_0's l1: 1.59512e+06 [42] valid_0's l1: 1.59286e+06 [43] valid_0's l1: 1.5929e+06 [44] valid_0's l1: 1.59173e+06 [45] valid_0's l1: 1.59163e+06 [46] valid_0's l1: 1.59149e+06 [47] valid_0's l1: 1.59111e+06 [48] valid_0's l1: 1.59168e+06 [49] valid_0's l1: 1.59049e+06 [50] valid_0's l1: 1.58867e+06 [51] valid_0's l1: 1.58963e+06 [52] valid_0's l1: 1.5878e+06 [53] valid_0's l1: 1.58834e+06 [54] valid_0's l1: 1.58963e+06 [55] valid_0's l1: 1.59054e+06 [56] valid_0's l1: 1.58928e+06 [57] valid_0's l1: 1.59034e+06 [58] valid_0's l1: 1.59245e+06 [59] valid_0's l1: 1.59305e+06 [60] valid_0's l1: 1.59228e+06 [61] valid_0's l1: 1.59105e+06 [62] valid_0's l1: 1.59135e+06 [63] valid_0's l1: 1.59307e+06 [64] valid_0's l1: 1.59467e+06 [65] valid_0's l1: 1.59252e+06 [66] valid_0's l1: 1.59273e+06 [67] valid_0's l1: 1.59195e+06 [68] valid_0's l1: 1.59278e+06 [69] valid_0's l1: 1.59117e+06 [70] valid_0's l1: 1.59206e+06 [71] valid_0's l1: 1.58878e+06 [72] valid_0's l1: 1.58795e+06 [73] valid_0's l1: 1.58921e+06 [74] valid_0's l1: 1.58924e+06 [75] valid_0's l1: 1.58805e+06 [76] valid_0's l1: 1.59015e+06 [77] valid_0's l1: 1.58963e+06 [78] valid_0's l1: 1.59027e+06 [79] valid_0's l1: 1.59127e+06 [80] valid_0's l1: 1.59145e+06 [81] valid_0's l1: 1.59056e+06 [82] valid_0's l1: 1.58941e+06 Early stopping, best iteration is: [52] valid_0's l1: 1.5878e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.91572e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.61074e+06 [3] valid_0's l1: 3.33753e+06 [4] valid_0's l1: 3.10152e+06 [5] valid_0's l1: 2.89582e+06 [6] valid_0's l1: 2.79571e+06 [7] valid_0's l1: 2.70656e+06 [8] valid_0's l1: 2.55707e+06 [9] valid_0's l1: 2.42782e+06 [10] valid_0's l1: 2.31229e+06 [11] valid_0's l1: 2.20818e+06 [12] valid_0's l1: 2.16335e+06 [13] valid_0's l1: 2.08003e+06 [14] valid_0's l1: 2.05301e+06 [15] valid_0's l1: 1.99207e+06 [16] valid_0's l1: 1.94133e+06 [17] valid_0's l1: 1.89339e+06 [18] valid_0's l1: 1.85478e+06 [19] valid_0's l1: 1.8147e+06 [20] valid_0's l1: 1.78487e+06 [21] valid_0's l1: 1.75924e+06 [22] valid_0's l1: 1.74016e+06 [23] valid_0's l1: 1.71891e+06 [24] valid_0's l1: 1.70106e+06 [25] valid_0's l1: 1.68439e+06 [26] valid_0's l1: 1.67383e+06 [27] valid_0's l1: 1.65878e+06 [28] valid_0's l1: 1.649e+06 [29] valid_0's l1: 1.63901e+06 [30] valid_0's l1: 1.62905e+06 [31] valid_0's l1: 1.62759e+06 [32] valid_0's l1: 1.61952e+06 [33] valid_0's l1: 1.61689e+06 [34] valid_0's l1: 1.61312e+06 [35] valid_0's l1: 1.60736e+06 [36] valid_0's l1: 1.60514e+06 [37] valid_0's l1: 1.60376e+06 [38] valid_0's l1: 1.60592e+06 [39] valid_0's l1: 1.60543e+06 [40] valid_0's l1: 1.59799e+06 [41] valid_0's l1: 1.59851e+06 [42] valid_0's l1: 1.59751e+06 [43] valid_0's l1: 1.59811e+06 [44] valid_0's l1: 1.59782e+06 [45] valid_0's l1: 1.59742e+06 [46] valid_0's l1: 1.59288e+06 [47] valid_0's l1: 1.59251e+06 [48] valid_0's l1: 1.59195e+06 [49] valid_0's l1: 1.58928e+06 [50] valid_0's l1: 1.59053e+06 [51] valid_0's l1: 1.59081e+06 [52] valid_0's l1: 1.59059e+06 [53] valid_0's l1: 1.59177e+06 [54] valid_0's l1: 1.59203e+06 [55] valid_0's l1: 1.59381e+06 [56] valid_0's l1: 1.59399e+06 [57] valid_0's l1: 1.59315e+06 [58] valid_0's l1: 1.59266e+06 [59] valid_0's l1: 1.59384e+06 [60] valid_0's l1: 1.59174e+06 [61] valid_0's l1: 1.58921e+06 [62] valid_0's l1: 1.58785e+06 [63] valid_0's l1: 1.58523e+06 [64] valid_0's l1: 1.58298e+06 [65] valid_0's l1: 1.58113e+06 [66] valid_0's l1: 1.58106e+06 [67] valid_0's l1: 1.58116e+06 [68] valid_0's l1: 1.57942e+06 [69] valid_0's l1: 1.58066e+06 [70] valid_0's l1: 1.58022e+06 [71] valid_0's l1: 1.5808e+06 [72] valid_0's l1: 1.58208e+06 [73] valid_0's l1: 1.58456e+06 [74] valid_0's l1: 1.58385e+06 [75] valid_0's l1: 1.58498e+06 [76] valid_0's l1: 1.58434e+06 [77] valid_0's l1: 1.58475e+06 [78] valid_0's l1: 1.58145e+06 [79] valid_0's l1: 1.58196e+06 [80] valid_0's l1: 1.58417e+06 [81] valid_0's l1: 1.58494e+06 [82] valid_0's l1: 1.58513e+06 [83] valid_0's l1: 1.58645e+06 [84] valid_0's l1: 1.58739e+06 [85] valid_0's l1: 1.58657e+06 [86] valid_0's l1: 1.58803e+06 [87] valid_0's l1: 1.58818e+06 [88] valid_0's l1: 1.58914e+06 [89] valid_0's l1: 1.58878e+06 [90] valid_0's l1: 1.58933e+06 [91] valid_0's l1: 1.58835e+06 [92] valid_0's l1: 1.5869e+06 [93] valid_0's l1: 1.58721e+06 [94] valid_0's l1: 1.58837e+06 [95] valid_0's l1: 1.58912e+06 [96] valid_0's l1: 1.58927e+06 [97] valid_0's l1: 1.58881e+06 [98] valid_0's l1: 1.59116e+06 Early stopping, best iteration is: [68] valid_0's l1: 1.57942e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.893e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.58967e+06 [3] valid_0's l1: 3.32468e+06 [4] valid_0's l1: 3.09825e+06 [5] valid_0's l1: 2.89071e+06 [6] valid_0's l1: 2.80154e+06 [7] valid_0's l1: 2.71644e+06 [8] valid_0's l1: 2.56741e+06 [9] valid_0's l1: 2.43756e+06 [10] valid_0's l1: 2.32643e+06 [11] valid_0's l1: 2.23079e+06 [12] valid_0's l1: 2.18948e+06 [13] valid_0's l1: 2.10749e+06 [14] valid_0's l1: 2.08242e+06 [15] valid_0's l1: 2.01493e+06 [16] valid_0's l1: 1.95632e+06 [17] valid_0's l1: 1.90485e+06 [18] valid_0's l1: 1.86042e+06 [19] valid_0's l1: 1.82396e+06 [20] valid_0's l1: 1.79462e+06 [21] valid_0's l1: 1.76725e+06 [22] valid_0's l1: 1.7403e+06 [23] valid_0's l1: 1.71626e+06 [24] valid_0's l1: 1.69631e+06 [25] valid_0's l1: 1.67999e+06 [26] valid_0's l1: 1.66406e+06 [27] valid_0's l1: 1.65339e+06 [28] valid_0's l1: 1.64006e+06 [29] valid_0's l1: 1.63299e+06 [30] valid_0's l1: 1.63076e+06 [31] valid_0's l1: 1.6258e+06 [32] valid_0's l1: 1.62122e+06 [33] valid_0's l1: 1.616e+06 [34] valid_0's l1: 1.61383e+06 [35] valid_0's l1: 1.61383e+06 [36] valid_0's l1: 1.60584e+06 [37] valid_0's l1: 1.60486e+06 [38] valid_0's l1: 1.60448e+06 [39] valid_0's l1: 1.59697e+06 [40] valid_0's l1: 1.5953e+06 [41] valid_0's l1: 1.59587e+06 [42] valid_0's l1: 1.59189e+06 [43] valid_0's l1: 1.59228e+06 [44] valid_0's l1: 1.58958e+06 [45] valid_0's l1: 1.58835e+06 [46] valid_0's l1: 1.5905e+06 [47] valid_0's l1: 1.59163e+06 [48] valid_0's l1: 1.58945e+06 [49] valid_0's l1: 1.59093e+06 [50] valid_0's l1: 1.58741e+06 [51] valid_0's l1: 1.58842e+06 [52] valid_0's l1: 1.58895e+06 [53] valid_0's l1: 1.59036e+06 [54] valid_0's l1: 1.59301e+06 [55] valid_0's l1: 1.59183e+06 [56] valid_0's l1: 1.58957e+06 [57] valid_0's l1: 1.59206e+06 [58] valid_0's l1: 1.5917e+06 [59] valid_0's l1: 1.59371e+06 [60] valid_0's l1: 1.59457e+06 [61] valid_0's l1: 1.59512e+06 [62] valid_0's l1: 1.59525e+06 [63] valid_0's l1: 1.59274e+06 [64] valid_0's l1: 1.5912e+06 [65] valid_0's l1: 1.59183e+06 [66] valid_0's l1: 1.59296e+06 [67] valid_0's l1: 1.5923e+06 [68] valid_0's l1: 1.59439e+06 [69] valid_0's l1: 1.59513e+06 [70] valid_0's l1: 1.59526e+06 [71] valid_0's l1: 1.59688e+06 [72] valid_0's l1: 1.59705e+06 [73] valid_0's l1: 1.59793e+06 [74] valid_0's l1: 1.59769e+06 [75] valid_0's l1: 1.59656e+06 [76] valid_0's l1: 1.59516e+06 [77] valid_0's l1: 1.59735e+06 [78] valid_0's l1: 1.59631e+06 [79] valid_0's l1: 1.5967e+06 [80] valid_0's l1: 1.59598e+06 Early stopping, best iteration is: [50] valid_0's l1: 1.58741e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.83364e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.5461e+06 [3] valid_0's l1: 3.29059e+06 [4] valid_0's l1: 3.05995e+06 [5] valid_0's l1: 2.86177e+06 [6] valid_0's l1: 2.77283e+06 [7] valid_0's l1: 2.69017e+06 [8] valid_0's l1: 2.54159e+06 [9] valid_0's l1: 2.41143e+06 [10] valid_0's l1: 2.30017e+06 [11] valid_0's l1: 2.20183e+06 [12] valid_0's l1: 2.15705e+06 [13] valid_0's l1: 2.07987e+06 [14] valid_0's l1: 2.05065e+06 [15] valid_0's l1: 1.98821e+06 [16] valid_0's l1: 1.93406e+06 [17] valid_0's l1: 1.88841e+06 [18] valid_0's l1: 1.8524e+06 [19] valid_0's l1: 1.81851e+06 [20] valid_0's l1: 1.78965e+06 [21] valid_0's l1: 1.76343e+06 [22] valid_0's l1: 1.74308e+06 [23] valid_0's l1: 1.7241e+06 [24] valid_0's l1: 1.70645e+06 [25] valid_0's l1: 1.69239e+06 [26] valid_0's l1: 1.67826e+06 [27] valid_0's l1: 1.66647e+06 [28] valid_0's l1: 1.65689e+06 [29] valid_0's l1: 1.64698e+06 [30] valid_0's l1: 1.63783e+06 [31] valid_0's l1: 1.62995e+06 [32] valid_0's l1: 1.62507e+06 [33] valid_0's l1: 1.62243e+06 [34] valid_0's l1: 1.61622e+06 [35] valid_0's l1: 1.61111e+06 [36] valid_0's l1: 1.6069e+06 [37] valid_0's l1: 1.6063e+06 [38] valid_0's l1: 1.60097e+06 [39] valid_0's l1: 1.60005e+06 [40] valid_0's l1: 1.59611e+06 [41] valid_0's l1: 1.59512e+06 [42] valid_0's l1: 1.59286e+06 [43] valid_0's l1: 1.5929e+06 [44] valid_0's l1: 1.59173e+06 [45] valid_0's l1: 1.59163e+06 [46] valid_0's l1: 1.59149e+06 [47] valid_0's l1: 1.59111e+06 [48] valid_0's l1: 1.59168e+06 [49] valid_0's l1: 1.59049e+06 [50] valid_0's l1: 1.58867e+06 [51] valid_0's l1: 1.58963e+06 [52] valid_0's l1: 1.5878e+06 [53] valid_0's l1: 1.58834e+06 [54] valid_0's l1: 1.58963e+06 [55] valid_0's l1: 1.59054e+06 [56] valid_0's l1: 1.58928e+06 [57] valid_0's l1: 1.59034e+06 [58] valid_0's l1: 1.59245e+06 [59] valid_0's l1: 1.59305e+06 [60] valid_0's l1: 1.59228e+06 [61] valid_0's l1: 1.59105e+06 [62] valid_0's l1: 1.59135e+06 [63] valid_0's l1: 1.59307e+06 [64] valid_0's l1: 1.59467e+06 [65] valid_0's l1: 1.59252e+06 [66] valid_0's l1: 1.59273e+06 [67] valid_0's l1: 1.59195e+06 [68] valid_0's l1: 1.59278e+06 [69] valid_0's l1: 1.59117e+06 [70] valid_0's l1: 1.59206e+06 [71] valid_0's l1: 1.58878e+06 [72] valid_0's l1: 1.58795e+06 [73] valid_0's l1: 1.58921e+06 [74] valid_0's l1: 1.58924e+06 [75] valid_0's l1: 1.58805e+06 [76] valid_0's l1: 1.59015e+06 [77] valid_0's l1: 1.58963e+06 [78] valid_0's l1: 1.59027e+06 [79] valid_0's l1: 1.59127e+06 [80] valid_0's l1: 1.59145e+06 [81] valid_0's l1: 1.59056e+06 [82] valid_0's l1: 1.58941e+06 Early stopping, best iteration is: [52] valid_0's l1: 1.5878e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.91572e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.61074e+06 [3] valid_0's l1: 3.33753e+06 [4] valid_0's l1: 3.10152e+06 [5] valid_0's l1: 2.89582e+06 [6] valid_0's l1: 2.79571e+06 [7] valid_0's l1: 2.70656e+06 [8] valid_0's l1: 2.55707e+06 [9] valid_0's l1: 2.42782e+06 [10] valid_0's l1: 2.31229e+06 [11] valid_0's l1: 2.20818e+06 [12] valid_0's l1: 2.16335e+06 [13] valid_0's l1: 2.08003e+06 [14] valid_0's l1: 2.05301e+06 [15] valid_0's l1: 1.99207e+06 [16] valid_0's l1: 1.94133e+06 [17] valid_0's l1: 1.89339e+06 [18] valid_0's l1: 1.85478e+06 [19] valid_0's l1: 1.8147e+06 [20] valid_0's l1: 1.78487e+06 [21] valid_0's l1: 1.75924e+06 [22] valid_0's l1: 1.74016e+06 [23] valid_0's l1: 1.71891e+06 [24] valid_0's l1: 1.70106e+06 [25] valid_0's l1: 1.68439e+06 [26] valid_0's l1: 1.67383e+06 [27] valid_0's l1: 1.65878e+06 [28] valid_0's l1: 1.649e+06 [29] valid_0's l1: 1.63901e+06 [30] valid_0's l1: 1.62905e+06 [31] valid_0's l1: 1.62759e+06 [32] valid_0's l1: 1.61952e+06 [33] valid_0's l1: 1.61689e+06 [34] valid_0's l1: 1.61312e+06 [35] valid_0's l1: 1.60736e+06 [36] valid_0's l1: 1.60514e+06 [37] valid_0's l1: 1.60376e+06 [38] valid_0's l1: 1.60592e+06 [39] valid_0's l1: 1.60543e+06 [40] valid_0's l1: 1.59799e+06 [41] valid_0's l1: 1.59851e+06 [42] valid_0's l1: 1.59751e+06 [43] valid_0's l1: 1.59811e+06 [44] valid_0's l1: 1.59782e+06 [45] valid_0's l1: 1.59742e+06 [46] valid_0's l1: 1.59288e+06 [47] valid_0's l1: 1.59251e+06 [48] valid_0's l1: 1.59195e+06 [49] valid_0's l1: 1.58928e+06 [50] valid_0's l1: 1.59053e+06 [51] valid_0's l1: 1.59081e+06 [52] valid_0's l1: 1.59059e+06 [53] valid_0's l1: 1.59177e+06 [54] valid_0's l1: 1.59203e+06 [55] valid_0's l1: 1.59381e+06 [56] valid_0's l1: 1.59399e+06 [57] valid_0's l1: 1.59315e+06 [58] valid_0's l1: 1.59266e+06 [59] valid_0's l1: 1.59384e+06 [60] valid_0's l1: 1.59174e+06 [61] valid_0's l1: 1.58921e+06 [62] valid_0's l1: 1.58785e+06 [63] valid_0's l1: 1.58523e+06 [64] valid_0's l1: 1.58298e+06 [65] valid_0's l1: 1.58113e+06 [66] valid_0's l1: 1.58106e+06 [67] valid_0's l1: 1.58116e+06 [68] valid_0's l1: 1.57942e+06 [69] valid_0's l1: 1.58066e+06 [70] valid_0's l1: 1.58022e+06 [71] valid_0's l1: 1.5808e+06 [72] valid_0's l1: 1.58208e+06 [73] valid_0's l1: 1.58456e+06 [74] valid_0's l1: 1.58385e+06 [75] valid_0's l1: 1.58498e+06 [76] valid_0's l1: 1.58434e+06 [77] valid_0's l1: 1.58475e+06 [78] valid_0's l1: 1.58145e+06 [79] valid_0's l1: 1.58196e+06 [80] valid_0's l1: 1.58417e+06 [81] valid_0's l1: 1.58494e+06 [82] valid_0's l1: 1.58513e+06 [83] valid_0's l1: 1.58645e+06 [84] valid_0's l1: 1.58739e+06 [85] valid_0's l1: 1.58657e+06 [86] valid_0's l1: 1.58803e+06 [87] valid_0's l1: 1.58818e+06 [88] valid_0's l1: 1.58914e+06 [89] valid_0's l1: 1.58878e+06 [90] valid_0's l1: 1.58933e+06 [91] valid_0's l1: 1.58835e+06 [92] valid_0's l1: 1.5869e+06 [93] valid_0's l1: 1.58721e+06 [94] valid_0's l1: 1.58837e+06 [95] valid_0's l1: 1.58912e+06 [96] valid_0's l1: 1.58927e+06 [97] valid_0's l1: 1.58881e+06 [98] valid_0's l1: 1.59116e+06 Early stopping, best iteration is: [68] valid_0's l1: 1.57942e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.893e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.58967e+06 [3] valid_0's l1: 3.32468e+06 [4] valid_0's l1: 3.09825e+06 [5] valid_0's l1: 2.89071e+06 [6] valid_0's l1: 2.80154e+06 [7] valid_0's l1: 2.71644e+06 [8] valid_0's l1: 2.56741e+06 [9] valid_0's l1: 2.43756e+06 [10] valid_0's l1: 2.32643e+06 [11] valid_0's l1: 2.23079e+06 [12] valid_0's l1: 2.18948e+06 [13] valid_0's l1: 2.10749e+06 [14] valid_0's l1: 2.08242e+06 [15] valid_0's l1: 2.01493e+06 [16] valid_0's l1: 1.95632e+06 [17] valid_0's l1: 1.90485e+06 [18] valid_0's l1: 1.86042e+06 [19] valid_0's l1: 1.82396e+06 [20] valid_0's l1: 1.79462e+06 [21] valid_0's l1: 1.76725e+06 [22] valid_0's l1: 1.7403e+06 [23] valid_0's l1: 1.71626e+06 [24] valid_0's l1: 1.69631e+06 [25] valid_0's l1: 1.67999e+06 [26] valid_0's l1: 1.66406e+06 [27] valid_0's l1: 1.65339e+06 [28] valid_0's l1: 1.64006e+06 [29] valid_0's l1: 1.63299e+06 [30] valid_0's l1: 1.63076e+06 [31] valid_0's l1: 1.6258e+06 [32] valid_0's l1: 1.62122e+06 [33] valid_0's l1: 1.616e+06 [34] valid_0's l1: 1.61383e+06 [35] valid_0's l1: 1.61383e+06 [36] valid_0's l1: 1.60584e+06 [37] valid_0's l1: 1.60486e+06 [38] valid_0's l1: 1.60448e+06 [39] valid_0's l1: 1.59697e+06 [40] valid_0's l1: 1.5953e+06 [41] valid_0's l1: 1.59587e+06 [42] valid_0's l1: 1.59189e+06 [43] valid_0's l1: 1.59228e+06 [44] valid_0's l1: 1.58958e+06 [45] valid_0's l1: 1.58835e+06 [46] valid_0's l1: 1.5905e+06 [47] valid_0's l1: 1.59163e+06 [48] valid_0's l1: 1.58945e+06 [49] valid_0's l1: 1.59093e+06 [50] valid_0's l1: 1.58741e+06 [51] valid_0's l1: 1.58842e+06 [52] valid_0's l1: 1.58895e+06 [53] valid_0's l1: 1.59036e+06 [54] valid_0's l1: 1.59301e+06 [55] valid_0's l1: 1.59183e+06 [56] valid_0's l1: 1.58957e+06 [57] valid_0's l1: 1.59206e+06 [58] valid_0's l1: 1.5917e+06 [59] valid_0's l1: 1.59371e+06 [60] valid_0's l1: 1.59457e+06 [61] valid_0's l1: 1.59512e+06 [62] valid_0's l1: 1.59525e+06 [63] valid_0's l1: 1.59274e+06 [64] valid_0's l1: 1.5912e+06 [65] valid_0's l1: 1.59183e+06 [66] valid_0's l1: 1.59296e+06 [67] valid_0's l1: 1.5923e+06 [68] valid_0's l1: 1.59439e+06 [69] valid_0's l1: 1.59513e+06 [70] valid_0's l1: 1.59526e+06 [71] valid_0's l1: 1.59688e+06 [72] valid_0's l1: 1.59705e+06 [73] valid_0's l1: 1.59793e+06 [74] valid_0's l1: 1.59769e+06 [75] valid_0's l1: 1.59656e+06 [76] valid_0's l1: 1.59516e+06 [77] valid_0's l1: 1.59735e+06 [78] valid_0's l1: 1.59631e+06 [79] valid_0's l1: 1.5967e+06 [80] valid_0's l1: 1.59598e+06 Early stopping, best iteration is: [50] valid_0's l1: 1.58741e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.83364e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.5461e+06 [3] valid_0's l1: 3.29059e+06 [4] valid_0's l1: 3.05995e+06 [5] valid_0's l1: 2.86177e+06 [6] valid_0's l1: 2.77283e+06 [7] valid_0's l1: 2.69017e+06 [8] valid_0's l1: 2.54159e+06 [9] valid_0's l1: 2.41143e+06 [10] valid_0's l1: 2.30017e+06 [11] valid_0's l1: 2.20183e+06 [12] valid_0's l1: 2.15705e+06 [13] valid_0's l1: 2.07987e+06 [14] valid_0's l1: 2.05065e+06 [15] valid_0's l1: 1.98821e+06 [16] valid_0's l1: 1.93406e+06 [17] valid_0's l1: 1.88841e+06 [18] valid_0's l1: 1.8524e+06 [19] valid_0's l1: 1.81851e+06 [20] valid_0's l1: 1.78965e+06 [21] valid_0's l1: 1.76343e+06 [22] valid_0's l1: 1.74308e+06 [23] valid_0's l1: 1.7241e+06 [24] valid_0's l1: 1.70645e+06 [25] valid_0's l1: 1.69239e+06 [26] valid_0's l1: 1.67826e+06 [27] valid_0's l1: 1.66647e+06 [28] valid_0's l1: 1.65689e+06 [29] valid_0's l1: 1.64698e+06 [30] valid_0's l1: 1.63783e+06 [31] valid_0's l1: 1.62995e+06 [32] valid_0's l1: 1.62507e+06 [33] valid_0's l1: 1.62243e+06 [34] valid_0's l1: 1.61622e+06 [35] valid_0's l1: 1.61111e+06 [36] valid_0's l1: 1.6069e+06 [37] valid_0's l1: 1.6063e+06 [38] valid_0's l1: 1.60097e+06 [39] valid_0's l1: 1.60005e+06 [40] valid_0's l1: 1.59611e+06 [41] valid_0's l1: 1.59512e+06 [42] valid_0's l1: 1.59286e+06 [43] valid_0's l1: 1.5929e+06 [44] valid_0's l1: 1.59173e+06 [45] valid_0's l1: 1.59163e+06 [46] valid_0's l1: 1.59149e+06 [47] valid_0's l1: 1.59111e+06 [48] valid_0's l1: 1.59168e+06 [49] valid_0's l1: 1.59049e+06 [50] valid_0's l1: 1.58867e+06 [51] valid_0's l1: 1.58963e+06 [52] valid_0's l1: 1.5878e+06 [53] valid_0's l1: 1.58834e+06 [54] valid_0's l1: 1.58963e+06 [55] valid_0's l1: 1.59054e+06 [56] valid_0's l1: 1.58928e+06 [57] valid_0's l1: 1.59034e+06 [58] valid_0's l1: 1.59245e+06 [59] valid_0's l1: 1.59305e+06 [60] valid_0's l1: 1.59228e+06 [61] valid_0's l1: 1.59105e+06 [62] valid_0's l1: 1.59135e+06 [63] valid_0's l1: 1.59307e+06 [64] valid_0's l1: 1.59467e+06 [65] valid_0's l1: 1.59252e+06 [66] valid_0's l1: 1.59273e+06 [67] valid_0's l1: 1.59195e+06 [68] valid_0's l1: 1.59278e+06 [69] valid_0's l1: 1.59117e+06 [70] valid_0's l1: 1.59206e+06 [71] valid_0's l1: 1.58878e+06 [72] valid_0's l1: 1.58795e+06 [73] valid_0's l1: 1.58921e+06 [74] valid_0's l1: 1.58924e+06 [75] valid_0's l1: 1.58805e+06 [76] valid_0's l1: 1.59015e+06 [77] valid_0's l1: 1.58963e+06 [78] valid_0's l1: 1.59027e+06 [79] valid_0's l1: 1.59127e+06 [80] valid_0's l1: 1.59145e+06 [81] valid_0's l1: 1.59056e+06 [82] valid_0's l1: 1.58941e+06 Early stopping, best iteration is: [52] valid_0's l1: 1.5878e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.91572e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.61074e+06 [3] valid_0's l1: 3.33753e+06 [4] valid_0's l1: 3.10152e+06 [5] valid_0's l1: 2.89582e+06 [6] valid_0's l1: 2.79571e+06 [7] valid_0's l1: 2.70656e+06 [8] valid_0's l1: 2.55707e+06 [9] valid_0's l1: 2.42782e+06 [10] valid_0's l1: 2.31229e+06 [11] valid_0's l1: 2.20818e+06 [12] valid_0's l1: 2.16335e+06 [13] valid_0's l1: 2.08003e+06 [14] valid_0's l1: 2.05301e+06 [15] valid_0's l1: 1.99207e+06 [16] valid_0's l1: 1.94133e+06 [17] valid_0's l1: 1.89339e+06 [18] valid_0's l1: 1.85478e+06 [19] valid_0's l1: 1.8147e+06 [20] valid_0's l1: 1.78487e+06 [21] valid_0's l1: 1.75924e+06 [22] valid_0's l1: 1.74016e+06 [23] valid_0's l1: 1.71891e+06 [24] valid_0's l1: 1.70106e+06 [25] valid_0's l1: 1.68439e+06 [26] valid_0's l1: 1.67383e+06 [27] valid_0's l1: 1.65878e+06 [28] valid_0's l1: 1.649e+06 [29] valid_0's l1: 1.63901e+06 [30] valid_0's l1: 1.62905e+06 [31] valid_0's l1: 1.62759e+06 [32] valid_0's l1: 1.61952e+06 [33] valid_0's l1: 1.61689e+06 [34] valid_0's l1: 1.61312e+06 [35] valid_0's l1: 1.60736e+06 [36] valid_0's l1: 1.60514e+06 [37] valid_0's l1: 1.60376e+06 [38] valid_0's l1: 1.60592e+06 [39] valid_0's l1: 1.60543e+06 [40] valid_0's l1: 1.59799e+06 [41] valid_0's l1: 1.59851e+06 [42] valid_0's l1: 1.59751e+06 [43] valid_0's l1: 1.59811e+06 [44] valid_0's l1: 1.59782e+06 [45] valid_0's l1: 1.59742e+06 [46] valid_0's l1: 1.59288e+06 [47] valid_0's l1: 1.59251e+06 [48] valid_0's l1: 1.59195e+06 [49] valid_0's l1: 1.58928e+06 [50] valid_0's l1: 1.59053e+06 [51] valid_0's l1: 1.59081e+06 [52] valid_0's l1: 1.59059e+06 [53] valid_0's l1: 1.59177e+06 [54] valid_0's l1: 1.59203e+06 [55] valid_0's l1: 1.59381e+06 [56] valid_0's l1: 1.59399e+06 [57] valid_0's l1: 1.59315e+06 [58] valid_0's l1: 1.59266e+06 [59] valid_0's l1: 1.59384e+06 [60] valid_0's l1: 1.59174e+06 [61] valid_0's l1: 1.58921e+06 [62] valid_0's l1: 1.58785e+06 [63] valid_0's l1: 1.58523e+06 [64] valid_0's l1: 1.58298e+06 [65] valid_0's l1: 1.58113e+06 [66] valid_0's l1: 1.58106e+06 [67] valid_0's l1: 1.58116e+06 [68] valid_0's l1: 1.57942e+06 [69] valid_0's l1: 1.58066e+06 [70] valid_0's l1: 1.58022e+06 [71] valid_0's l1: 1.5808e+06 [72] valid_0's l1: 1.58208e+06 [73] valid_0's l1: 1.58456e+06 [74] valid_0's l1: 1.58385e+06 [75] valid_0's l1: 1.58498e+06 [76] valid_0's l1: 1.58434e+06 [77] valid_0's l1: 1.58475e+06 [78] valid_0's l1: 1.58145e+06 [79] valid_0's l1: 1.58196e+06 [80] valid_0's l1: 1.58417e+06 [81] valid_0's l1: 1.58494e+06 [82] valid_0's l1: 1.58513e+06 [83] valid_0's l1: 1.58645e+06 [84] valid_0's l1: 1.58739e+06 [85] valid_0's l1: 1.58657e+06 [86] valid_0's l1: 1.58803e+06 [87] valid_0's l1: 1.58818e+06 [88] valid_0's l1: 1.58914e+06 [89] valid_0's l1: 1.58878e+06 [90] valid_0's l1: 1.58933e+06 [91] valid_0's l1: 1.58835e+06 [92] valid_0's l1: 1.5869e+06 [93] valid_0's l1: 1.58721e+06 [94] valid_0's l1: 1.58837e+06 [95] valid_0's l1: 1.58912e+06 [96] valid_0's l1: 1.58927e+06 [97] valid_0's l1: 1.58881e+06 [98] valid_0's l1: 1.59116e+06 Early stopping, best iteration is: [68] valid_0's l1: 1.57942e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.893e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.58967e+06 [3] valid_0's l1: 3.32468e+06 [4] valid_0's l1: 3.09825e+06 [5] valid_0's l1: 2.89071e+06 [6] valid_0's l1: 2.80154e+06 [7] valid_0's l1: 2.71644e+06 [8] valid_0's l1: 2.56741e+06 [9] valid_0's l1: 2.43756e+06 [10] valid_0's l1: 2.32643e+06 [11] valid_0's l1: 2.23079e+06 [12] valid_0's l1: 2.18948e+06 [13] valid_0's l1: 2.10749e+06 [14] valid_0's l1: 2.08242e+06 [15] valid_0's l1: 2.01493e+06 [16] valid_0's l1: 1.95632e+06 [17] valid_0's l1: 1.90485e+06 [18] valid_0's l1: 1.86042e+06 [19] valid_0's l1: 1.82396e+06 [20] valid_0's l1: 1.79462e+06 [21] valid_0's l1: 1.76725e+06 [22] valid_0's l1: 1.7403e+06 [23] valid_0's l1: 1.71626e+06 [24] valid_0's l1: 1.69631e+06 [25] valid_0's l1: 1.67999e+06 [26] valid_0's l1: 1.66406e+06 [27] valid_0's l1: 1.65339e+06 [28] valid_0's l1: 1.64006e+06 [29] valid_0's l1: 1.63299e+06 [30] valid_0's l1: 1.63076e+06 [31] valid_0's l1: 1.6258e+06 [32] valid_0's l1: 1.62122e+06 [33] valid_0's l1: 1.616e+06 [34] valid_0's l1: 1.61383e+06 [35] valid_0's l1: 1.61383e+06 [36] valid_0's l1: 1.60584e+06 [37] valid_0's l1: 1.60486e+06 [38] valid_0's l1: 1.60448e+06 [39] valid_0's l1: 1.59697e+06 [40] valid_0's l1: 1.5953e+06 [41] valid_0's l1: 1.59587e+06 [42] valid_0's l1: 1.59189e+06 [43] valid_0's l1: 1.59228e+06 [44] valid_0's l1: 1.58958e+06 [45] valid_0's l1: 1.58835e+06 [46] valid_0's l1: 1.5905e+06 [47] valid_0's l1: 1.59163e+06 [48] valid_0's l1: 1.58945e+06 [49] valid_0's l1: 1.59093e+06 [50] valid_0's l1: 1.58741e+06 [51] valid_0's l1: 1.58842e+06 [52] valid_0's l1: 1.58895e+06 [53] valid_0's l1: 1.59036e+06 [54] valid_0's l1: 1.59301e+06 [55] valid_0's l1: 1.59183e+06 [56] valid_0's l1: 1.58957e+06 [57] valid_0's l1: 1.59206e+06 [58] valid_0's l1: 1.5917e+06 [59] valid_0's l1: 1.59371e+06 [60] valid_0's l1: 1.59457e+06 [61] valid_0's l1: 1.59512e+06 [62] valid_0's l1: 1.59525e+06 [63] valid_0's l1: 1.59274e+06 [64] valid_0's l1: 1.5912e+06 [65] valid_0's l1: 1.59183e+06 [66] valid_0's l1: 1.59296e+06 [67] valid_0's l1: 1.5923e+06 [68] valid_0's l1: 1.59439e+06 [69] valid_0's l1: 1.59513e+06 [70] valid_0's l1: 1.59526e+06 [71] valid_0's l1: 1.59688e+06 [72] valid_0's l1: 1.59705e+06 [73] valid_0's l1: 1.59793e+06 [74] valid_0's l1: 1.59769e+06 [75] valid_0's l1: 1.59656e+06 [76] valid_0's l1: 1.59516e+06 [77] valid_0's l1: 1.59735e+06 [78] valid_0's l1: 1.59631e+06 [79] valid_0's l1: 1.5967e+06 [80] valid_0's l1: 1.59598e+06 Early stopping, best iteration is: [50] valid_0's l1: 1.58741e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.83364e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.5461e+06 [3] valid_0's l1: 3.29059e+06 [4] valid_0's l1: 3.05995e+06 [5] valid_0's l1: 2.86177e+06 [6] valid_0's l1: 2.77283e+06 [7] valid_0's l1: 2.69017e+06 [8] valid_0's l1: 2.54159e+06 [9] valid_0's l1: 2.41143e+06 [10] valid_0's l1: 2.30017e+06 [11] valid_0's l1: 2.20183e+06 [12] valid_0's l1: 2.15705e+06 [13] valid_0's l1: 2.07987e+06 [14] valid_0's l1: 2.05065e+06 [15] valid_0's l1: 1.98821e+06 [16] valid_0's l1: 1.93406e+06 [17] valid_0's l1: 1.88841e+06 [18] valid_0's l1: 1.8524e+06 [19] valid_0's l1: 1.81851e+06 [20] valid_0's l1: 1.78965e+06 [21] valid_0's l1: 1.76343e+06 [22] valid_0's l1: 1.74308e+06 [23] valid_0's l1: 1.7241e+06 [24] valid_0's l1: 1.70645e+06 [25] valid_0's l1: 1.69239e+06 [26] valid_0's l1: 1.67826e+06 [27] valid_0's l1: 1.66647e+06 [28] valid_0's l1: 1.65689e+06 [29] valid_0's l1: 1.64698e+06 [30] valid_0's l1: 1.63783e+06 [31] valid_0's l1: 1.62995e+06 [32] valid_0's l1: 1.62507e+06 [33] valid_0's l1: 1.62243e+06 [34] valid_0's l1: 1.61622e+06 [35] valid_0's l1: 1.61111e+06 [36] valid_0's l1: 1.6069e+06 [37] valid_0's l1: 1.6063e+06 [38] valid_0's l1: 1.60097e+06 [39] valid_0's l1: 1.60005e+06 [40] valid_0's l1: 1.59611e+06 [41] valid_0's l1: 1.59512e+06 [42] valid_0's l1: 1.59286e+06 [43] valid_0's l1: 1.5929e+06 [44] valid_0's l1: 1.59173e+06 [45] valid_0's l1: 1.59163e+06 [46] valid_0's l1: 1.59149e+06 [47] valid_0's l1: 1.59111e+06 [48] valid_0's l1: 1.59168e+06 [49] valid_0's l1: 1.59049e+06 [50] valid_0's l1: 1.58867e+06 [51] valid_0's l1: 1.58963e+06 [52] valid_0's l1: 1.5878e+06 [53] valid_0's l1: 1.58834e+06 [54] valid_0's l1: 1.58963e+06 [55] valid_0's l1: 1.59054e+06 [56] valid_0's l1: 1.58928e+06 [57] valid_0's l1: 1.59034e+06 [58] valid_0's l1: 1.59245e+06 [59] valid_0's l1: 1.59305e+06 [60] valid_0's l1: 1.59228e+06 [61] valid_0's l1: 1.59105e+06 [62] valid_0's l1: 1.59135e+06 [63] valid_0's l1: 1.59307e+06 [64] valid_0's l1: 1.59467e+06 [65] valid_0's l1: 1.59252e+06 [66] valid_0's l1: 1.59273e+06 [67] valid_0's l1: 1.59195e+06 [68] valid_0's l1: 1.59278e+06 [69] valid_0's l1: 1.59117e+06 [70] valid_0's l1: 1.59206e+06 [71] valid_0's l1: 1.58878e+06 [72] valid_0's l1: 1.58795e+06 [73] valid_0's l1: 1.58921e+06 [74] valid_0's l1: 1.58924e+06 [75] valid_0's l1: 1.58805e+06 [76] valid_0's l1: 1.59015e+06 [77] valid_0's l1: 1.58963e+06 [78] valid_0's l1: 1.59027e+06 [79] valid_0's l1: 1.59127e+06 [80] valid_0's l1: 1.59145e+06 [81] valid_0's l1: 1.59056e+06 [82] valid_0's l1: 1.58941e+06 Early stopping, best iteration is: [52] valid_0's l1: 1.5878e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.91572e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.61074e+06 [3] valid_0's l1: 3.33753e+06 [4] valid_0's l1: 3.10152e+06 [5] valid_0's l1: 2.89582e+06 [6] valid_0's l1: 2.79571e+06 [7] valid_0's l1: 2.70656e+06 [8] valid_0's l1: 2.55707e+06 [9] valid_0's l1: 2.42782e+06 [10] valid_0's l1: 2.31229e+06 [11] valid_0's l1: 2.20818e+06 [12] valid_0's l1: 2.16335e+06 [13] valid_0's l1: 2.08003e+06 [14] valid_0's l1: 2.05301e+06 [15] valid_0's l1: 1.99207e+06 [16] valid_0's l1: 1.94133e+06 [17] valid_0's l1: 1.89339e+06 [18] valid_0's l1: 1.85478e+06 [19] valid_0's l1: 1.8147e+06 [20] valid_0's l1: 1.78487e+06 [21] valid_0's l1: 1.75924e+06 [22] valid_0's l1: 1.74016e+06 [23] valid_0's l1: 1.71891e+06 [24] valid_0's l1: 1.70106e+06 [25] valid_0's l1: 1.68439e+06 [26] valid_0's l1: 1.67383e+06 [27] valid_0's l1: 1.65878e+06 [28] valid_0's l1: 1.649e+06 [29] valid_0's l1: 1.63901e+06 [30] valid_0's l1: 1.62905e+06 [31] valid_0's l1: 1.62759e+06 [32] valid_0's l1: 1.61952e+06 [33] valid_0's l1: 1.61689e+06 [34] valid_0's l1: 1.61312e+06 [35] valid_0's l1: 1.60736e+06 [36] valid_0's l1: 1.60514e+06 [37] valid_0's l1: 1.60376e+06 [38] valid_0's l1: 1.60592e+06 [39] valid_0's l1: 1.60543e+06 [40] valid_0's l1: 1.59799e+06 [41] valid_0's l1: 1.59851e+06 [42] valid_0's l1: 1.59751e+06 [43] valid_0's l1: 1.59811e+06 [44] valid_0's l1: 1.59782e+06 [45] valid_0's l1: 1.59742e+06 [46] valid_0's l1: 1.59288e+06 [47] valid_0's l1: 1.59251e+06 [48] valid_0's l1: 1.59195e+06 [49] valid_0's l1: 1.58928e+06 [50] valid_0's l1: 1.59053e+06 [51] valid_0's l1: 1.59081e+06 [52] valid_0's l1: 1.59059e+06 [53] valid_0's l1: 1.59177e+06 [54] valid_0's l1: 1.59203e+06 [55] valid_0's l1: 1.59381e+06 [56] valid_0's l1: 1.59399e+06 [57] valid_0's l1: 1.59315e+06 [58] valid_0's l1: 1.59266e+06 [59] valid_0's l1: 1.59384e+06 [60] valid_0's l1: 1.59174e+06 [61] valid_0's l1: 1.58921e+06 [62] valid_0's l1: 1.58785e+06 [63] valid_0's l1: 1.58523e+06 [64] valid_0's l1: 1.58298e+06 [65] valid_0's l1: 1.58113e+06 [66] valid_0's l1: 1.58106e+06 [67] valid_0's l1: 1.58116e+06 [68] valid_0's l1: 1.57942e+06 [69] valid_0's l1: 1.58066e+06 [70] valid_0's l1: 1.58022e+06 [71] valid_0's l1: 1.5808e+06 [72] valid_0's l1: 1.58208e+06 [73] valid_0's l1: 1.58456e+06 [74] valid_0's l1: 1.58385e+06 [75] valid_0's l1: 1.58498e+06 [76] valid_0's l1: 1.58434e+06 [77] valid_0's l1: 1.58475e+06 [78] valid_0's l1: 1.58145e+06 [79] valid_0's l1: 1.58196e+06 [80] valid_0's l1: 1.58417e+06 [81] valid_0's l1: 1.58494e+06 [82] valid_0's l1: 1.58513e+06 [83] valid_0's l1: 1.58645e+06 [84] valid_0's l1: 1.58739e+06 [85] valid_0's l1: 1.58657e+06 [86] valid_0's l1: 1.58803e+06 [87] valid_0's l1: 1.58818e+06 [88] valid_0's l1: 1.58914e+06 [89] valid_0's l1: 1.58878e+06 [90] valid_0's l1: 1.58933e+06 [91] valid_0's l1: 1.58835e+06 [92] valid_0's l1: 1.5869e+06 [93] valid_0's l1: 1.58721e+06 [94] valid_0's l1: 1.58837e+06 [95] valid_0's l1: 1.58912e+06 [96] valid_0's l1: 1.58927e+06 [97] valid_0's l1: 1.58881e+06 [98] valid_0's l1: 1.59116e+06 Early stopping, best iteration is: [68] valid_0's l1: 1.57942e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.893e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.58967e+06 [3] valid_0's l1: 3.32468e+06 [4] valid_0's l1: 3.09825e+06 [5] valid_0's l1: 2.89071e+06 [6] valid_0's l1: 2.80154e+06 [7] valid_0's l1: 2.71644e+06 [8] valid_0's l1: 2.56741e+06 [9] valid_0's l1: 2.43756e+06 [10] valid_0's l1: 2.32643e+06 [11] valid_0's l1: 2.23079e+06 [12] valid_0's l1: 2.18948e+06 [13] valid_0's l1: 2.10749e+06 [14] valid_0's l1: 2.08242e+06 [15] valid_0's l1: 2.01493e+06 [16] valid_0's l1: 1.95632e+06 [17] valid_0's l1: 1.90485e+06 [18] valid_0's l1: 1.86042e+06 [19] valid_0's l1: 1.82396e+06 [20] valid_0's l1: 1.79462e+06 [21] valid_0's l1: 1.76725e+06 [22] valid_0's l1: 1.7403e+06 [23] valid_0's l1: 1.71626e+06 [24] valid_0's l1: 1.69631e+06 [25] valid_0's l1: 1.67999e+06 [26] valid_0's l1: 1.66406e+06 [27] valid_0's l1: 1.65339e+06 [28] valid_0's l1: 1.64006e+06 [29] valid_0's l1: 1.63299e+06 [30] valid_0's l1: 1.63076e+06 [31] valid_0's l1: 1.6258e+06 [32] valid_0's l1: 1.62122e+06 [33] valid_0's l1: 1.616e+06 [34] valid_0's l1: 1.61383e+06 [35] valid_0's l1: 1.61383e+06 [36] valid_0's l1: 1.60584e+06 [37] valid_0's l1: 1.60486e+06 [38] valid_0's l1: 1.60448e+06 [39] valid_0's l1: 1.59697e+06 [40] valid_0's l1: 1.5953e+06 [41] valid_0's l1: 1.59587e+06 [42] valid_0's l1: 1.59189e+06 [43] valid_0's l1: 1.59228e+06 [44] valid_0's l1: 1.58958e+06 [45] valid_0's l1: 1.58835e+06 [46] valid_0's l1: 1.5905e+06 [47] valid_0's l1: 1.59163e+06 [48] valid_0's l1: 1.58945e+06 [49] valid_0's l1: 1.59093e+06 [50] valid_0's l1: 1.58741e+06 [51] valid_0's l1: 1.58842e+06 [52] valid_0's l1: 1.58895e+06 [53] valid_0's l1: 1.59036e+06 [54] valid_0's l1: 1.59301e+06 [55] valid_0's l1: 1.59183e+06 [56] valid_0's l1: 1.58957e+06 [57] valid_0's l1: 1.59206e+06 [58] valid_0's l1: 1.5917e+06 [59] valid_0's l1: 1.59371e+06 [60] valid_0's l1: 1.59457e+06 [61] valid_0's l1: 1.59512e+06 [62] valid_0's l1: 1.59525e+06 [63] valid_0's l1: 1.59274e+06 [64] valid_0's l1: 1.5912e+06 [65] valid_0's l1: 1.59183e+06 [66] valid_0's l1: 1.59296e+06 [67] valid_0's l1: 1.5923e+06 [68] valid_0's l1: 1.59439e+06 [69] valid_0's l1: 1.59513e+06 [70] valid_0's l1: 1.59526e+06 [71] valid_0's l1: 1.59688e+06 [72] valid_0's l1: 1.59705e+06 [73] valid_0's l1: 1.59793e+06 [74] valid_0's l1: 1.59769e+06 [75] valid_0's l1: 1.59656e+06 [76] valid_0's l1: 1.59516e+06 [77] valid_0's l1: 1.59735e+06 [78] valid_0's l1: 1.59631e+06 [79] valid_0's l1: 1.5967e+06 [80] valid_0's l1: 1.59598e+06 Early stopping, best iteration is: [50] valid_0's l1: 1.58741e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.83364e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.5461e+06 [3] valid_0's l1: 3.29059e+06 [4] valid_0's l1: 3.05995e+06 [5] valid_0's l1: 2.86177e+06 [6] valid_0's l1: 2.77283e+06 [7] valid_0's l1: 2.69017e+06 [8] valid_0's l1: 2.54159e+06 [9] valid_0's l1: 2.41143e+06 [10] valid_0's l1: 2.30017e+06 [11] valid_0's l1: 2.20183e+06 [12] valid_0's l1: 2.15705e+06 [13] valid_0's l1: 2.07987e+06 [14] valid_0's l1: 2.05065e+06 [15] valid_0's l1: 1.98821e+06 [16] valid_0's l1: 1.93406e+06 [17] valid_0's l1: 1.88841e+06 [18] valid_0's l1: 1.8524e+06 [19] valid_0's l1: 1.81851e+06 [20] valid_0's l1: 1.78965e+06 [21] valid_0's l1: 1.76343e+06 [22] valid_0's l1: 1.74308e+06 [23] valid_0's l1: 1.7241e+06 [24] valid_0's l1: 1.70645e+06 [25] valid_0's l1: 1.69239e+06 [26] valid_0's l1: 1.67826e+06 [27] valid_0's l1: 1.66647e+06 [28] valid_0's l1: 1.65689e+06 [29] valid_0's l1: 1.64698e+06 [30] valid_0's l1: 1.63783e+06 [31] valid_0's l1: 1.62995e+06 [32] valid_0's l1: 1.62507e+06 [33] valid_0's l1: 1.62243e+06 [34] valid_0's l1: 1.61622e+06 [35] valid_0's l1: 1.61111e+06 [36] valid_0's l1: 1.6069e+06 [37] valid_0's l1: 1.6063e+06 [38] valid_0's l1: 1.60097e+06 [39] valid_0's l1: 1.60005e+06 [40] valid_0's l1: 1.59611e+06 [41] valid_0's l1: 1.59512e+06 [42] valid_0's l1: 1.59286e+06 [43] valid_0's l1: 1.5929e+06 [44] valid_0's l1: 1.59173e+06 [45] valid_0's l1: 1.59163e+06 [46] valid_0's l1: 1.59149e+06 [47] valid_0's l1: 1.59111e+06 [48] valid_0's l1: 1.59168e+06 [49] valid_0's l1: 1.59049e+06 [50] valid_0's l1: 1.58867e+06 [51] valid_0's l1: 1.58963e+06 [52] valid_0's l1: 1.5878e+06 [53] valid_0's l1: 1.58834e+06 [54] valid_0's l1: 1.58963e+06 [55] valid_0's l1: 1.59054e+06 [56] valid_0's l1: 1.58928e+06 [57] valid_0's l1: 1.59034e+06 [58] valid_0's l1: 1.59245e+06 [59] valid_0's l1: 1.59305e+06 [60] valid_0's l1: 1.59228e+06 [61] valid_0's l1: 1.59105e+06 [62] valid_0's l1: 1.59135e+06 [63] valid_0's l1: 1.59307e+06 [64] valid_0's l1: 1.59467e+06 [65] valid_0's l1: 1.59252e+06 [66] valid_0's l1: 1.59273e+06 [67] valid_0's l1: 1.59195e+06 [68] valid_0's l1: 1.59278e+06 [69] valid_0's l1: 1.59117e+06 [70] valid_0's l1: 1.59206e+06 [71] valid_0's l1: 1.58878e+06 [72] valid_0's l1: 1.58795e+06 [73] valid_0's l1: 1.58921e+06 [74] valid_0's l1: 1.58924e+06 [75] valid_0's l1: 1.58805e+06 [76] valid_0's l1: 1.59015e+06 [77] valid_0's l1: 1.58963e+06 [78] valid_0's l1: 1.59027e+06 [79] valid_0's l1: 1.59127e+06 [80] valid_0's l1: 1.59145e+06 [81] valid_0's l1: 1.59056e+06 [82] valid_0's l1: 1.58941e+06 Early stopping, best iteration is: [52] valid_0's l1: 1.5878e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.91572e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.61074e+06 [3] valid_0's l1: 3.33753e+06 [4] valid_0's l1: 3.10152e+06 [5] valid_0's l1: 2.89582e+06 [6] valid_0's l1: 2.79571e+06 [7] valid_0's l1: 2.70656e+06 [8] valid_0's l1: 2.55707e+06 [9] valid_0's l1: 2.42782e+06 [10] valid_0's l1: 2.31229e+06 [11] valid_0's l1: 2.20818e+06 [12] valid_0's l1: 2.16335e+06 [13] valid_0's l1: 2.08003e+06 [14] valid_0's l1: 2.05301e+06 [15] valid_0's l1: 1.99207e+06 [16] valid_0's l1: 1.94133e+06 [17] valid_0's l1: 1.89339e+06 [18] valid_0's l1: 1.85478e+06 [19] valid_0's l1: 1.8147e+06 [20] valid_0's l1: 1.78487e+06 [21] valid_0's l1: 1.75924e+06 [22] valid_0's l1: 1.74016e+06 [23] valid_0's l1: 1.71891e+06 [24] valid_0's l1: 1.70106e+06 [25] valid_0's l1: 1.68439e+06 [26] valid_0's l1: 1.67383e+06 [27] valid_0's l1: 1.65878e+06 [28] valid_0's l1: 1.649e+06 [29] valid_0's l1: 1.63901e+06 [30] valid_0's l1: 1.62905e+06 [31] valid_0's l1: 1.62759e+06 [32] valid_0's l1: 1.61952e+06 [33] valid_0's l1: 1.61689e+06 [34] valid_0's l1: 1.61312e+06 [35] valid_0's l1: 1.60736e+06 [36] valid_0's l1: 1.60514e+06 [37] valid_0's l1: 1.60376e+06 [38] valid_0's l1: 1.60592e+06 [39] valid_0's l1: 1.60543e+06 [40] valid_0's l1: 1.59799e+06 [41] valid_0's l1: 1.59851e+06 [42] valid_0's l1: 1.59751e+06 [43] valid_0's l1: 1.59811e+06 [44] valid_0's l1: 1.59782e+06 [45] valid_0's l1: 1.59742e+06 [46] valid_0's l1: 1.59288e+06 [47] valid_0's l1: 1.59251e+06 [48] valid_0's l1: 1.59195e+06 [49] valid_0's l1: 1.58928e+06 [50] valid_0's l1: 1.59053e+06 [51] valid_0's l1: 1.59081e+06 [52] valid_0's l1: 1.59059e+06 [53] valid_0's l1: 1.59177e+06 [54] valid_0's l1: 1.59203e+06 [55] valid_0's l1: 1.59381e+06 [56] valid_0's l1: 1.59399e+06 [57] valid_0's l1: 1.59315e+06 [58] valid_0's l1: 1.59266e+06 [59] valid_0's l1: 1.59384e+06 [60] valid_0's l1: 1.59174e+06 [61] valid_0's l1: 1.58921e+06 [62] valid_0's l1: 1.58785e+06 [63] valid_0's l1: 1.58523e+06 [64] valid_0's l1: 1.58298e+06 [65] valid_0's l1: 1.58113e+06 [66] valid_0's l1: 1.58106e+06 [67] valid_0's l1: 1.58116e+06 [68] valid_0's l1: 1.57942e+06 [69] valid_0's l1: 1.58066e+06 [70] valid_0's l1: 1.58022e+06 [71] valid_0's l1: 1.5808e+06 [72] valid_0's l1: 1.58208e+06 [73] valid_0's l1: 1.58456e+06 [74] valid_0's l1: 1.58385e+06 [75] valid_0's l1: 1.58498e+06 [76] valid_0's l1: 1.58434e+06 [77] valid_0's l1: 1.58475e+06 [78] valid_0's l1: 1.58145e+06 [79] valid_0's l1: 1.58196e+06 [80] valid_0's l1: 1.58417e+06 [81] valid_0's l1: 1.58494e+06 [82] valid_0's l1: 1.58513e+06 [83] valid_0's l1: 1.58645e+06 [84] valid_0's l1: 1.58739e+06 [85] valid_0's l1: 1.58657e+06 [86] valid_0's l1: 1.58803e+06 [87] valid_0's l1: 1.58818e+06 [88] valid_0's l1: 1.58914e+06 [89] valid_0's l1: 1.58878e+06 [90] valid_0's l1: 1.58933e+06 [91] valid_0's l1: 1.58835e+06 [92] valid_0's l1: 1.5869e+06 [93] valid_0's l1: 1.58721e+06 [94] valid_0's l1: 1.58837e+06 [95] valid_0's l1: 1.58912e+06 [96] valid_0's l1: 1.58927e+06 [97] valid_0's l1: 1.58881e+06 [98] valid_0's l1: 1.59116e+06 Early stopping, best iteration is: [68] valid_0's l1: 1.57942e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.893e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.58967e+06 [3] valid_0's l1: 3.32468e+06 [4] valid_0's l1: 3.09825e+06 [5] valid_0's l1: 2.89071e+06 [6] valid_0's l1: 2.80154e+06 [7] valid_0's l1: 2.71644e+06 [8] valid_0's l1: 2.56741e+06 [9] valid_0's l1: 2.43756e+06 [10] valid_0's l1: 2.32643e+06 [11] valid_0's l1: 2.23079e+06 [12] valid_0's l1: 2.18948e+06 [13] valid_0's l1: 2.10749e+06 [14] valid_0's l1: 2.08242e+06 [15] valid_0's l1: 2.01493e+06 [16] valid_0's l1: 1.95632e+06 [17] valid_0's l1: 1.90485e+06 [18] valid_0's l1: 1.86042e+06 [19] valid_0's l1: 1.82396e+06 [20] valid_0's l1: 1.79462e+06 [21] valid_0's l1: 1.76725e+06 [22] valid_0's l1: 1.7403e+06 [23] valid_0's l1: 1.71626e+06 [24] valid_0's l1: 1.69631e+06 [25] valid_0's l1: 1.67999e+06 [26] valid_0's l1: 1.66406e+06 [27] valid_0's l1: 1.65339e+06 [28] valid_0's l1: 1.64006e+06 [29] valid_0's l1: 1.63299e+06 [30] valid_0's l1: 1.63076e+06 [31] valid_0's l1: 1.6258e+06 [32] valid_0's l1: 1.62122e+06 [33] valid_0's l1: 1.616e+06 [34] valid_0's l1: 1.61383e+06 [35] valid_0's l1: 1.61383e+06 [36] valid_0's l1: 1.60584e+06 [37] valid_0's l1: 1.60486e+06 [38] valid_0's l1: 1.60448e+06 [39] valid_0's l1: 1.59697e+06 [40] valid_0's l1: 1.5953e+06 [41] valid_0's l1: 1.59587e+06 [42] valid_0's l1: 1.59189e+06 [43] valid_0's l1: 1.59228e+06 [44] valid_0's l1: 1.58958e+06 [45] valid_0's l1: 1.58835e+06 [46] valid_0's l1: 1.5905e+06 [47] valid_0's l1: 1.59163e+06 [48] valid_0's l1: 1.58945e+06 [49] valid_0's l1: 1.59093e+06 [50] valid_0's l1: 1.58741e+06 [51] valid_0's l1: 1.58842e+06 [52] valid_0's l1: 1.58895e+06 [53] valid_0's l1: 1.59036e+06 [54] valid_0's l1: 1.59301e+06 [55] valid_0's l1: 1.59183e+06 [56] valid_0's l1: 1.58957e+06 [57] valid_0's l1: 1.59206e+06 [58] valid_0's l1: 1.5917e+06 [59] valid_0's l1: 1.59371e+06 [60] valid_0's l1: 1.59457e+06 [61] valid_0's l1: 1.59512e+06 [62] valid_0's l1: 1.59525e+06 [63] valid_0's l1: 1.59274e+06 [64] valid_0's l1: 1.5912e+06 [65] valid_0's l1: 1.59183e+06 [66] valid_0's l1: 1.59296e+06 [67] valid_0's l1: 1.5923e+06 [68] valid_0's l1: 1.59439e+06 [69] valid_0's l1: 1.59513e+06 [70] valid_0's l1: 1.59526e+06 [71] valid_0's l1: 1.59688e+06 [72] valid_0's l1: 1.59705e+06 [73] valid_0's l1: 1.59793e+06 [74] valid_0's l1: 1.59769e+06 [75] valid_0's l1: 1.59656e+06 [76] valid_0's l1: 1.59516e+06 [77] valid_0's l1: 1.59735e+06 [78] valid_0's l1: 1.59631e+06 [79] valid_0's l1: 1.5967e+06 [80] valid_0's l1: 1.59598e+06 Early stopping, best iteration is: [50] valid_0's l1: 1.58741e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.83364e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.5461e+06 [3] valid_0's l1: 3.29059e+06 [4] valid_0's l1: 3.05995e+06 [5] valid_0's l1: 2.86177e+06 [6] valid_0's l1: 2.77283e+06 [7] valid_0's l1: 2.69017e+06 [8] valid_0's l1: 2.54159e+06 [9] valid_0's l1: 2.41143e+06 [10] valid_0's l1: 2.30017e+06 [11] valid_0's l1: 2.20183e+06 [12] valid_0's l1: 2.15705e+06 [13] valid_0's l1: 2.07987e+06 [14] valid_0's l1: 2.05065e+06 [15] valid_0's l1: 1.98821e+06 [16] valid_0's l1: 1.93406e+06 [17] valid_0's l1: 1.88841e+06 [18] valid_0's l1: 1.8524e+06 [19] valid_0's l1: 1.81851e+06 [20] valid_0's l1: 1.78965e+06 [21] valid_0's l1: 1.76343e+06 [22] valid_0's l1: 1.74308e+06 [23] valid_0's l1: 1.7241e+06 [24] valid_0's l1: 1.70645e+06 [25] valid_0's l1: 1.69239e+06 [26] valid_0's l1: 1.67826e+06 [27] valid_0's l1: 1.66647e+06 [28] valid_0's l1: 1.65689e+06 [29] valid_0's l1: 1.64698e+06 [30] valid_0's l1: 1.63783e+06 [31] valid_0's l1: 1.62995e+06 [32] valid_0's l1: 1.62507e+06 [33] valid_0's l1: 1.62243e+06 [34] valid_0's l1: 1.61622e+06 [35] valid_0's l1: 1.61111e+06 [36] valid_0's l1: 1.6069e+06 [37] valid_0's l1: 1.6063e+06 [38] valid_0's l1: 1.60097e+06 [39] valid_0's l1: 1.60005e+06 [40] valid_0's l1: 1.59611e+06 [41] valid_0's l1: 1.59512e+06 [42] valid_0's l1: 1.59286e+06 [43] valid_0's l1: 1.5929e+06 [44] valid_0's l1: 1.59173e+06 [45] valid_0's l1: 1.59163e+06 [46] valid_0's l1: 1.59149e+06 [47] valid_0's l1: 1.59111e+06 [48] valid_0's l1: 1.59168e+06 [49] valid_0's l1: 1.59049e+06 [50] valid_0's l1: 1.58867e+06 [51] valid_0's l1: 1.58963e+06 [52] valid_0's l1: 1.5878e+06 [53] valid_0's l1: 1.58834e+06 [54] valid_0's l1: 1.58963e+06 [55] valid_0's l1: 1.59054e+06 [56] valid_0's l1: 1.58928e+06 [57] valid_0's l1: 1.59034e+06 [58] valid_0's l1: 1.59245e+06 [59] valid_0's l1: 1.59305e+06 [60] valid_0's l1: 1.59228e+06 [61] valid_0's l1: 1.59105e+06 [62] valid_0's l1: 1.59135e+06 [63] valid_0's l1: 1.59307e+06 [64] valid_0's l1: 1.59467e+06 [65] valid_0's l1: 1.59252e+06 [66] valid_0's l1: 1.59273e+06 [67] valid_0's l1: 1.59195e+06 [68] valid_0's l1: 1.59278e+06 [69] valid_0's l1: 1.59117e+06 [70] valid_0's l1: 1.59206e+06 [71] valid_0's l1: 1.58878e+06 [72] valid_0's l1: 1.58795e+06 [73] valid_0's l1: 1.58921e+06 [74] valid_0's l1: 1.58924e+06 [75] valid_0's l1: 1.58805e+06 [76] valid_0's l1: 1.59015e+06 [77] valid_0's l1: 1.58963e+06 [78] valid_0's l1: 1.59027e+06 [79] valid_0's l1: 1.59127e+06 [80] valid_0's l1: 1.59145e+06 [81] valid_0's l1: 1.59056e+06 [82] valid_0's l1: 1.58941e+06 Early stopping, best iteration is: [52] valid_0's l1: 1.5878e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.91572e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.61074e+06 [3] valid_0's l1: 3.33753e+06 [4] valid_0's l1: 3.10152e+06 [5] valid_0's l1: 2.89582e+06 [6] valid_0's l1: 2.79571e+06 [7] valid_0's l1: 2.70656e+06 [8] valid_0's l1: 2.55707e+06 [9] valid_0's l1: 2.42782e+06 [10] valid_0's l1: 2.31229e+06 [11] valid_0's l1: 2.20818e+06 [12] valid_0's l1: 2.16335e+06 [13] valid_0's l1: 2.08003e+06 [14] valid_0's l1: 2.05301e+06 [15] valid_0's l1: 1.99207e+06 [16] valid_0's l1: 1.94133e+06 [17] valid_0's l1: 1.89339e+06 [18] valid_0's l1: 1.85478e+06 [19] valid_0's l1: 1.8147e+06 [20] valid_0's l1: 1.78487e+06 [21] valid_0's l1: 1.75924e+06 [22] valid_0's l1: 1.74016e+06 [23] valid_0's l1: 1.71891e+06 [24] valid_0's l1: 1.70106e+06 [25] valid_0's l1: 1.68439e+06 [26] valid_0's l1: 1.67383e+06 [27] valid_0's l1: 1.65878e+06 [28] valid_0's l1: 1.649e+06 [29] valid_0's l1: 1.63901e+06 [30] valid_0's l1: 1.62905e+06 [31] valid_0's l1: 1.62759e+06 [32] valid_0's l1: 1.61952e+06 [33] valid_0's l1: 1.61689e+06 [34] valid_0's l1: 1.61312e+06 [35] valid_0's l1: 1.60736e+06 [36] valid_0's l1: 1.60514e+06 [37] valid_0's l1: 1.60376e+06 [38] valid_0's l1: 1.60592e+06 [39] valid_0's l1: 1.60543e+06 [40] valid_0's l1: 1.59799e+06 [41] valid_0's l1: 1.59851e+06 [42] valid_0's l1: 1.59751e+06 [43] valid_0's l1: 1.59811e+06 [44] valid_0's l1: 1.59782e+06 [45] valid_0's l1: 1.59742e+06 [46] valid_0's l1: 1.59288e+06 [47] valid_0's l1: 1.59251e+06 [48] valid_0's l1: 1.59195e+06 [49] valid_0's l1: 1.58928e+06 [50] valid_0's l1: 1.59053e+06 [51] valid_0's l1: 1.59081e+06 [52] valid_0's l1: 1.59059e+06 [53] valid_0's l1: 1.59177e+06 [54] valid_0's l1: 1.59203e+06 [55] valid_0's l1: 1.59381e+06 [56] valid_0's l1: 1.59399e+06 [57] valid_0's l1: 1.59315e+06 [58] valid_0's l1: 1.59266e+06 [59] valid_0's l1: 1.59384e+06 [60] valid_0's l1: 1.59174e+06 [61] valid_0's l1: 1.58921e+06 [62] valid_0's l1: 1.58785e+06 [63] valid_0's l1: 1.58523e+06 [64] valid_0's l1: 1.58298e+06 [65] valid_0's l1: 1.58113e+06 [66] valid_0's l1: 1.58106e+06 [67] valid_0's l1: 1.58116e+06 [68] valid_0's l1: 1.57942e+06 [69] valid_0's l1: 1.58066e+06 [70] valid_0's l1: 1.58022e+06 [71] valid_0's l1: 1.5808e+06 [72] valid_0's l1: 1.58208e+06 [73] valid_0's l1: 1.58456e+06 [74] valid_0's l1: 1.58385e+06 [75] valid_0's l1: 1.58498e+06 [76] valid_0's l1: 1.58434e+06 [77] valid_0's l1: 1.58475e+06 [78] valid_0's l1: 1.58145e+06 [79] valid_0's l1: 1.58196e+06 [80] valid_0's l1: 1.58417e+06 [81] valid_0's l1: 1.58494e+06 [82] valid_0's l1: 1.58513e+06 [83] valid_0's l1: 1.58645e+06 [84] valid_0's l1: 1.58739e+06 [85] valid_0's l1: 1.58657e+06 [86] valid_0's l1: 1.58803e+06 [87] valid_0's l1: 1.58818e+06 [88] valid_0's l1: 1.58914e+06 [89] valid_0's l1: 1.58878e+06 [90] valid_0's l1: 1.58933e+06 [91] valid_0's l1: 1.58835e+06 [92] valid_0's l1: 1.5869e+06 [93] valid_0's l1: 1.58721e+06 [94] valid_0's l1: 1.58837e+06 [95] valid_0's l1: 1.58912e+06 [96] valid_0's l1: 1.58927e+06 [97] valid_0's l1: 1.58881e+06 [98] valid_0's l1: 1.59116e+06 Early stopping, best iteration is: [68] valid_0's l1: 1.57942e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.69405e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.1598e+06 [3] valid_0's l1: 2.76355e+06 [4] valid_0's l1: 2.45909e+06 [5] valid_0's l1: 2.24837e+06 [6] valid_0's l1: 2.17429e+06 [7] valid_0's l1: 2.11153e+06 [8] valid_0's l1: 1.98392e+06 [9] valid_0's l1: 1.89989e+06 [10] valid_0's l1: 1.8382e+06 [11] valid_0's l1: 1.78986e+06 [12] valid_0's l1: 1.77362e+06 [13] valid_0's l1: 1.74998e+06 [14] valid_0's l1: 1.7455e+06 [15] valid_0's l1: 1.72846e+06 [16] valid_0's l1: 1.71675e+06 [17] valid_0's l1: 1.71e+06 [18] valid_0's l1: 1.70856e+06 [19] valid_0's l1: 1.70935e+06 [20] valid_0's l1: 1.70615e+06 [21] valid_0's l1: 1.71159e+06 [22] valid_0's l1: 1.71179e+06 [23] valid_0's l1: 1.71527e+06 [24] valid_0's l1: 1.72176e+06 [25] valid_0's l1: 1.72737e+06 [26] valid_0's l1: 1.72886e+06 [27] valid_0's l1: 1.7298e+06 [28] valid_0's l1: 1.73337e+06 [29] valid_0's l1: 1.73864e+06 [30] valid_0's l1: 1.73277e+06 [31] valid_0's l1: 1.74082e+06 [32] valid_0's l1: 1.74536e+06 [33] valid_0's l1: 1.7446e+06 [34] valid_0's l1: 1.74336e+06 [35] valid_0's l1: 1.74879e+06 [36] valid_0's l1: 1.74662e+06 [37] valid_0's l1: 1.75018e+06 [38] valid_0's l1: 1.75158e+06 [39] valid_0's l1: 1.75496e+06 [40] valid_0's l1: 1.75484e+06 [41] valid_0's l1: 1.75695e+06 [42] valid_0's l1: 1.75627e+06 [43] valid_0's l1: 1.75882e+06 [44] valid_0's l1: 1.76028e+06 [45] valid_0's l1: 1.7591e+06 [46] valid_0's l1: 1.75985e+06 [47] valid_0's l1: 1.76132e+06 [48] valid_0's l1: 1.76081e+06 [49] valid_0's l1: 1.75987e+06 [50] valid_0's l1: 1.76197e+06 Early stopping, best iteration is: [20] valid_0's l1: 1.70615e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.65312e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.14754e+06 [3] valid_0's l1: 2.74253e+06 [4] valid_0's l1: 2.44702e+06 [5] valid_0's l1: 2.23118e+06 [6] valid_0's l1: 2.1436e+06 [7] valid_0's l1: 2.07788e+06 [8] valid_0's l1: 1.96328e+06 [9] valid_0's l1: 1.88086e+06 [10] valid_0's l1: 1.81434e+06 [11] valid_0's l1: 1.77025e+06 [12] valid_0's l1: 1.7582e+06 [13] valid_0's l1: 1.7324e+06 [14] valid_0's l1: 1.7307e+06 [15] valid_0's l1: 1.71914e+06 [16] valid_0's l1: 1.70847e+06 [17] valid_0's l1: 1.71082e+06 [18] valid_0's l1: 1.70652e+06 [19] valid_0's l1: 1.70882e+06 [20] valid_0's l1: 1.70653e+06 [21] valid_0's l1: 1.71105e+06 [22] valid_0's l1: 1.71115e+06 [23] valid_0's l1: 1.71315e+06 [24] valid_0's l1: 1.71157e+06 [25] valid_0's l1: 1.72015e+06 [26] valid_0's l1: 1.72486e+06 [27] valid_0's l1: 1.72282e+06 [28] valid_0's l1: 1.72413e+06 [29] valid_0's l1: 1.72733e+06 [30] valid_0's l1: 1.72706e+06 [31] valid_0's l1: 1.72835e+06 [32] valid_0's l1: 1.72733e+06 [33] valid_0's l1: 1.73047e+06 [34] valid_0's l1: 1.72756e+06 [35] valid_0's l1: 1.72728e+06 [36] valid_0's l1: 1.73103e+06 [37] valid_0's l1: 1.73193e+06 [38] valid_0's l1: 1.73618e+06 [39] valid_0's l1: 1.73471e+06 [40] valid_0's l1: 1.73887e+06 [41] valid_0's l1: 1.73759e+06 [42] valid_0's l1: 1.74282e+06 [43] valid_0's l1: 1.74739e+06 [44] valid_0's l1: 1.74498e+06 [45] valid_0's l1: 1.74684e+06 [46] valid_0's l1: 1.75015e+06 [47] valid_0's l1: 1.7487e+06 [48] valid_0's l1: 1.74989e+06 Early stopping, best iteration is: [18] valid_0's l1: 1.70652e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.72204e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.17054e+06 [3] valid_0's l1: 2.74648e+06 [4] valid_0's l1: 2.44099e+06 [5] valid_0's l1: 2.22643e+06 [6] valid_0's l1: 2.13949e+06 [7] valid_0's l1: 2.07147e+06 [8] valid_0's l1: 1.96228e+06 [9] valid_0's l1: 1.88236e+06 [10] valid_0's l1: 1.82206e+06 [11] valid_0's l1: 1.7695e+06 [12] valid_0's l1: 1.75469e+06 [13] valid_0's l1: 1.72994e+06 [14] valid_0's l1: 1.73225e+06 [15] valid_0's l1: 1.71957e+06 [16] valid_0's l1: 1.70141e+06 [17] valid_0's l1: 1.68815e+06 [18] valid_0's l1: 1.69655e+06 [19] valid_0's l1: 1.69964e+06 [20] valid_0's l1: 1.6935e+06 [21] valid_0's l1: 1.6906e+06 [22] valid_0's l1: 1.69327e+06 [23] valid_0's l1: 1.69228e+06 [24] valid_0's l1: 1.69521e+06 [25] valid_0's l1: 1.69694e+06 [26] valid_0's l1: 1.69293e+06 [27] valid_0's l1: 1.69643e+06 [28] valid_0's l1: 1.69572e+06 [29] valid_0's l1: 1.69559e+06 [30] valid_0's l1: 1.69489e+06 [31] valid_0's l1: 1.70033e+06 [32] valid_0's l1: 1.70343e+06 [33] valid_0's l1: 1.70433e+06 [34] valid_0's l1: 1.70409e+06 [35] valid_0's l1: 1.70692e+06 [36] valid_0's l1: 1.70936e+06 [37] valid_0's l1: 1.71489e+06 [38] valid_0's l1: 1.71212e+06 [39] valid_0's l1: 1.71647e+06 [40] valid_0's l1: 1.71969e+06 [41] valid_0's l1: 1.72057e+06 [42] valid_0's l1: 1.72345e+06 [43] valid_0's l1: 1.72367e+06 [44] valid_0's l1: 1.72635e+06 [45] valid_0's l1: 1.73191e+06 [46] valid_0's l1: 1.7332e+06 [47] valid_0's l1: 1.73282e+06 Early stopping, best iteration is: [17] valid_0's l1: 1.68815e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.69457e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.16036e+06 [3] valid_0's l1: 2.76039e+06 [4] valid_0's l1: 2.46011e+06 [5] valid_0's l1: 2.25014e+06 [6] valid_0's l1: 2.17619e+06 [7] valid_0's l1: 2.12057e+06 [8] valid_0's l1: 1.99046e+06 [9] valid_0's l1: 1.90537e+06 [10] valid_0's l1: 1.84246e+06 [11] valid_0's l1: 1.79557e+06 [12] valid_0's l1: 1.77552e+06 [13] valid_0's l1: 1.7552e+06 [14] valid_0's l1: 1.75783e+06 [15] valid_0's l1: 1.74009e+06 [16] valid_0's l1: 1.72329e+06 [17] valid_0's l1: 1.71541e+06 [18] valid_0's l1: 1.71353e+06 [19] valid_0's l1: 1.71455e+06 [20] valid_0's l1: 1.70729e+06 [21] valid_0's l1: 1.71074e+06 [22] valid_0's l1: 1.71576e+06 [23] valid_0's l1: 1.71677e+06 [24] valid_0's l1: 1.72341e+06 [25] valid_0's l1: 1.72883e+06 [26] valid_0's l1: 1.72534e+06 [27] valid_0's l1: 1.72912e+06 [28] valid_0's l1: 1.73368e+06 [29] valid_0's l1: 1.73878e+06 [30] valid_0's l1: 1.73599e+06 [31] valid_0's l1: 1.73795e+06 [32] valid_0's l1: 1.74221e+06 [33] valid_0's l1: 1.74634e+06 [34] valid_0's l1: 1.74628e+06 [35] valid_0's l1: 1.75163e+06 [36] valid_0's l1: 1.75402e+06 [37] valid_0's l1: 1.75099e+06 [38] valid_0's l1: 1.75159e+06 [39] valid_0's l1: 1.75533e+06 [40] valid_0's l1: 1.75661e+06 [41] valid_0's l1: 1.7594e+06 [42] valid_0's l1: 1.75635e+06 [43] valid_0's l1: 1.75724e+06 [44] valid_0's l1: 1.7619e+06 [45] valid_0's l1: 1.7619e+06 [46] valid_0's l1: 1.76091e+06 [47] valid_0's l1: 1.76298e+06 [48] valid_0's l1: 1.76366e+06 [49] valid_0's l1: 1.76831e+06 [50] valid_0's l1: 1.77015e+06 Early stopping, best iteration is: [20] valid_0's l1: 1.70729e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.65467e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.14936e+06 [3] valid_0's l1: 2.7456e+06 [4] valid_0's l1: 2.44883e+06 [5] valid_0's l1: 2.23091e+06 [6] valid_0's l1: 2.13898e+06 [7] valid_0's l1: 2.07805e+06 [8] valid_0's l1: 1.97044e+06 [9] valid_0's l1: 1.89433e+06 [10] valid_0's l1: 1.8338e+06 [11] valid_0's l1: 1.79255e+06 [12] valid_0's l1: 1.78759e+06 [13] valid_0's l1: 1.75872e+06 [14] valid_0's l1: 1.75191e+06 [15] valid_0's l1: 1.74271e+06 [16] valid_0's l1: 1.73072e+06 [17] valid_0's l1: 1.72693e+06 [18] valid_0's l1: 1.72805e+06 [19] valid_0's l1: 1.72976e+06 [20] valid_0's l1: 1.72195e+06 [21] valid_0's l1: 1.72643e+06 [22] valid_0's l1: 1.72262e+06 [23] valid_0's l1: 1.7236e+06 [24] valid_0's l1: 1.72047e+06 [25] valid_0's l1: 1.72379e+06 [26] valid_0's l1: 1.72899e+06 [27] valid_0's l1: 1.72603e+06 [28] valid_0's l1: 1.72886e+06 [29] valid_0's l1: 1.72794e+06 [30] valid_0's l1: 1.72986e+06 [31] valid_0's l1: 1.73508e+06 [32] valid_0's l1: 1.73161e+06 [33] valid_0's l1: 1.73361e+06 [34] valid_0's l1: 1.72988e+06 [35] valid_0's l1: 1.73199e+06 [36] valid_0's l1: 1.73268e+06 [37] valid_0's l1: 1.73617e+06 [38] valid_0's l1: 1.73751e+06 [39] valid_0's l1: 1.73535e+06 [40] valid_0's l1: 1.73826e+06 [41] valid_0's l1: 1.74188e+06 [42] valid_0's l1: 1.74079e+06 [43] valid_0's l1: 1.74727e+06 [44] valid_0's l1: 1.74386e+06 [45] valid_0's l1: 1.7466e+06 [46] valid_0's l1: 1.74465e+06 [47] valid_0's l1: 1.74705e+06 [48] valid_0's l1: 1.74613e+06 [49] valid_0's l1: 1.74901e+06 [50] valid_0's l1: 1.74705e+06 [51] valid_0's l1: 1.74809e+06 [52] valid_0's l1: 1.7492e+06 [53] valid_0's l1: 1.75047e+06 [54] valid_0's l1: 1.7508e+06 Early stopping, best iteration is: [24] valid_0's l1: 1.72047e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.72212e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.16752e+06 [3] valid_0's l1: 2.74658e+06 [4] valid_0's l1: 2.44054e+06 [5] valid_0's l1: 2.22549e+06 [6] valid_0's l1: 2.14158e+06 [7] valid_0's l1: 2.08181e+06 [8] valid_0's l1: 1.97017e+06 [9] valid_0's l1: 1.89011e+06 [10] valid_0's l1: 1.83723e+06 [11] valid_0's l1: 1.78618e+06 [12] valid_0's l1: 1.77151e+06 [13] valid_0's l1: 1.74297e+06 [14] valid_0's l1: 1.74896e+06 [15] valid_0's l1: 1.73828e+06 [16] valid_0's l1: 1.72576e+06 [17] valid_0's l1: 1.71663e+06 [18] valid_0's l1: 1.71738e+06 [19] valid_0's l1: 1.71398e+06 [20] valid_0's l1: 1.70916e+06 [21] valid_0's l1: 1.7073e+06 [22] valid_0's l1: 1.71162e+06 [23] valid_0's l1: 1.71532e+06 [24] valid_0's l1: 1.71218e+06 [25] valid_0's l1: 1.71308e+06 [26] valid_0's l1: 1.71093e+06 [27] valid_0's l1: 1.71039e+06 [28] valid_0's l1: 1.71325e+06 [29] valid_0's l1: 1.71475e+06 [30] valid_0's l1: 1.71906e+06 [31] valid_0's l1: 1.72529e+06 [32] valid_0's l1: 1.7273e+06 [33] valid_0's l1: 1.72672e+06 [34] valid_0's l1: 1.72911e+06 [35] valid_0's l1: 1.73121e+06 [36] valid_0's l1: 1.72691e+06 [37] valid_0's l1: 1.72494e+06 [38] valid_0's l1: 1.72654e+06 [39] valid_0's l1: 1.72893e+06 [40] valid_0's l1: 1.72876e+06 [41] valid_0's l1: 1.72963e+06 [42] valid_0's l1: 1.73378e+06 [43] valid_0's l1: 1.73461e+06 [44] valid_0's l1: 1.73571e+06 [45] valid_0's l1: 1.73816e+06 [46] valid_0's l1: 1.74112e+06 [47] valid_0's l1: 1.74109e+06 [48] valid_0's l1: 1.74229e+06 [49] valid_0's l1: 1.74099e+06 [50] valid_0's l1: 1.74137e+06 [51] valid_0's l1: 1.74264e+06 Early stopping, best iteration is: [21] valid_0's l1: 1.7073e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.69405e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.1598e+06 [3] valid_0's l1: 2.76355e+06 [4] valid_0's l1: 2.45909e+06 [5] valid_0's l1: 2.24837e+06 [6] valid_0's l1: 2.17429e+06 [7] valid_0's l1: 2.11153e+06 [8] valid_0's l1: 1.98392e+06 [9] valid_0's l1: 1.89989e+06 [10] valid_0's l1: 1.8382e+06 [11] valid_0's l1: 1.78986e+06 [12] valid_0's l1: 1.77362e+06 [13] valid_0's l1: 1.74998e+06 [14] valid_0's l1: 1.7455e+06 [15] valid_0's l1: 1.72846e+06 [16] valid_0's l1: 1.71675e+06 [17] valid_0's l1: 1.71e+06 [18] valid_0's l1: 1.70856e+06 [19] valid_0's l1: 1.70935e+06 [20] valid_0's l1: 1.70615e+06 [21] valid_0's l1: 1.71159e+06 [22] valid_0's l1: 1.71179e+06 [23] valid_0's l1: 1.71527e+06 [24] valid_0's l1: 1.72176e+06 [25] valid_0's l1: 1.72737e+06 [26] valid_0's l1: 1.72886e+06 [27] valid_0's l1: 1.7298e+06 [28] valid_0's l1: 1.73337e+06 [29] valid_0's l1: 1.73864e+06 [30] valid_0's l1: 1.73277e+06 [31] valid_0's l1: 1.74082e+06 [32] valid_0's l1: 1.74536e+06 [33] valid_0's l1: 1.7446e+06 [34] valid_0's l1: 1.74336e+06 [35] valid_0's l1: 1.74879e+06 [36] valid_0's l1: 1.74662e+06 [37] valid_0's l1: 1.75018e+06 [38] valid_0's l1: 1.75158e+06 [39] valid_0's l1: 1.75496e+06 [40] valid_0's l1: 1.75484e+06 [41] valid_0's l1: 1.75695e+06 [42] valid_0's l1: 1.75627e+06 [43] valid_0's l1: 1.75882e+06 [44] valid_0's l1: 1.76028e+06 [45] valid_0's l1: 1.7591e+06 [46] valid_0's l1: 1.75985e+06 [47] valid_0's l1: 1.76132e+06 [48] valid_0's l1: 1.76081e+06 [49] valid_0's l1: 1.75987e+06 [50] valid_0's l1: 1.76197e+06 Early stopping, best iteration is: [20] valid_0's l1: 1.70615e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.65312e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.14754e+06 [3] valid_0's l1: 2.74253e+06 [4] valid_0's l1: 2.44702e+06 [5] valid_0's l1: 2.23118e+06 [6] valid_0's l1: 2.1436e+06 [7] valid_0's l1: 2.07788e+06 [8] valid_0's l1: 1.96328e+06 [9] valid_0's l1: 1.88086e+06 [10] valid_0's l1: 1.81434e+06 [11] valid_0's l1: 1.77025e+06 [12] valid_0's l1: 1.7582e+06 [13] valid_0's l1: 1.7324e+06 [14] valid_0's l1: 1.7307e+06 [15] valid_0's l1: 1.71914e+06 [16] valid_0's l1: 1.70847e+06 [17] valid_0's l1: 1.71082e+06 [18] valid_0's l1: 1.70652e+06 [19] valid_0's l1: 1.70882e+06 [20] valid_0's l1: 1.70653e+06 [21] valid_0's l1: 1.71105e+06 [22] valid_0's l1: 1.71115e+06 [23] valid_0's l1: 1.71315e+06 [24] valid_0's l1: 1.71157e+06 [25] valid_0's l1: 1.72015e+06 [26] valid_0's l1: 1.72486e+06 [27] valid_0's l1: 1.72282e+06 [28] valid_0's l1: 1.72413e+06 [29] valid_0's l1: 1.72733e+06 [30] valid_0's l1: 1.72706e+06 [31] valid_0's l1: 1.72835e+06 [32] valid_0's l1: 1.72733e+06 [33] valid_0's l1: 1.73047e+06 [34] valid_0's l1: 1.72756e+06 [35] valid_0's l1: 1.72728e+06 [36] valid_0's l1: 1.73103e+06 [37] valid_0's l1: 1.73193e+06 [38] valid_0's l1: 1.73618e+06 [39] valid_0's l1: 1.73471e+06 [40] valid_0's l1: 1.73887e+06 [41] valid_0's l1: 1.73759e+06 [42] valid_0's l1: 1.74282e+06 [43] valid_0's l1: 1.74739e+06 [44] valid_0's l1: 1.74498e+06 [45] valid_0's l1: 1.74684e+06 [46] valid_0's l1: 1.75015e+06 [47] valid_0's l1: 1.7487e+06 [48] valid_0's l1: 1.74989e+06 Early stopping, best iteration is: [18] valid_0's l1: 1.70652e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.72204e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.17054e+06 [3] valid_0's l1: 2.74648e+06 [4] valid_0's l1: 2.44099e+06 [5] valid_0's l1: 2.22643e+06 [6] valid_0's l1: 2.13949e+06 [7] valid_0's l1: 2.07147e+06 [8] valid_0's l1: 1.96228e+06 [9] valid_0's l1: 1.88236e+06 [10] valid_0's l1: 1.82206e+06 [11] valid_0's l1: 1.7695e+06 [12] valid_0's l1: 1.75469e+06 [13] valid_0's l1: 1.72994e+06 [14] valid_0's l1: 1.73225e+06 [15] valid_0's l1: 1.71957e+06 [16] valid_0's l1: 1.70141e+06 [17] valid_0's l1: 1.68815e+06 [18] valid_0's l1: 1.69655e+06 [19] valid_0's l1: 1.69964e+06 [20] valid_0's l1: 1.6935e+06 [21] valid_0's l1: 1.6906e+06 [22] valid_0's l1: 1.69327e+06 [23] valid_0's l1: 1.69228e+06 [24] valid_0's l1: 1.69521e+06 [25] valid_0's l1: 1.69694e+06 [26] valid_0's l1: 1.69293e+06 [27] valid_0's l1: 1.69643e+06 [28] valid_0's l1: 1.69572e+06 [29] valid_0's l1: 1.69559e+06 [30] valid_0's l1: 1.69489e+06 [31] valid_0's l1: 1.70033e+06 [32] valid_0's l1: 1.70343e+06 [33] valid_0's l1: 1.70433e+06 [34] valid_0's l1: 1.70409e+06 [35] valid_0's l1: 1.70692e+06 [36] valid_0's l1: 1.70936e+06 [37] valid_0's l1: 1.71489e+06 [38] valid_0's l1: 1.71212e+06 [39] valid_0's l1: 1.71647e+06 [40] valid_0's l1: 1.71969e+06 [41] valid_0's l1: 1.72057e+06 [42] valid_0's l1: 1.72345e+06 [43] valid_0's l1: 1.72367e+06 [44] valid_0's l1: 1.72635e+06 [45] valid_0's l1: 1.73191e+06 [46] valid_0's l1: 1.7332e+06 [47] valid_0's l1: 1.73282e+06 Early stopping, best iteration is: [17] valid_0's l1: 1.68815e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.69457e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.16036e+06 [3] valid_0's l1: 2.76039e+06 [4] valid_0's l1: 2.46011e+06 [5] valid_0's l1: 2.25014e+06 [6] valid_0's l1: 2.17619e+06 [7] valid_0's l1: 2.12057e+06 [8] valid_0's l1: 1.99046e+06 [9] valid_0's l1: 1.90537e+06 [10] valid_0's l1: 1.84246e+06 [11] valid_0's l1: 1.79557e+06 [12] valid_0's l1: 1.77552e+06 [13] valid_0's l1: 1.7552e+06 [14] valid_0's l1: 1.75783e+06 [15] valid_0's l1: 1.74009e+06 [16] valid_0's l1: 1.72329e+06 [17] valid_0's l1: 1.71541e+06 [18] valid_0's l1: 1.71353e+06 [19] valid_0's l1: 1.71455e+06 [20] valid_0's l1: 1.70729e+06 [21] valid_0's l1: 1.71074e+06 [22] valid_0's l1: 1.71576e+06 [23] valid_0's l1: 1.71677e+06 [24] valid_0's l1: 1.72341e+06 [25] valid_0's l1: 1.72883e+06 [26] valid_0's l1: 1.72534e+06 [27] valid_0's l1: 1.72912e+06 [28] valid_0's l1: 1.73368e+06 [29] valid_0's l1: 1.73878e+06 [30] valid_0's l1: 1.73599e+06 [31] valid_0's l1: 1.73795e+06 [32] valid_0's l1: 1.74221e+06 [33] valid_0's l1: 1.74634e+06 [34] valid_0's l1: 1.74628e+06 [35] valid_0's l1: 1.75163e+06 [36] valid_0's l1: 1.75402e+06 [37] valid_0's l1: 1.75099e+06 [38] valid_0's l1: 1.75159e+06 [39] valid_0's l1: 1.75533e+06 [40] valid_0's l1: 1.75661e+06 [41] valid_0's l1: 1.7594e+06 [42] valid_0's l1: 1.75635e+06 [43] valid_0's l1: 1.75724e+06 [44] valid_0's l1: 1.7619e+06 [45] valid_0's l1: 1.7619e+06 [46] valid_0's l1: 1.76091e+06 [47] valid_0's l1: 1.76298e+06 [48] valid_0's l1: 1.76366e+06 [49] valid_0's l1: 1.76831e+06 [50] valid_0's l1: 1.77015e+06 Early stopping, best iteration is: [20] valid_0's l1: 1.70729e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.65467e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.14936e+06 [3] valid_0's l1: 2.7456e+06 [4] valid_0's l1: 2.44883e+06 [5] valid_0's l1: 2.23091e+06 [6] valid_0's l1: 2.13898e+06 [7] valid_0's l1: 2.07805e+06 [8] valid_0's l1: 1.97044e+06 [9] valid_0's l1: 1.89433e+06 [10] valid_0's l1: 1.8338e+06 [11] valid_0's l1: 1.79255e+06 [12] valid_0's l1: 1.78759e+06 [13] valid_0's l1: 1.75872e+06 [14] valid_0's l1: 1.75191e+06 [15] valid_0's l1: 1.74271e+06 [16] valid_0's l1: 1.73072e+06 [17] valid_0's l1: 1.72693e+06 [18] valid_0's l1: 1.72805e+06 [19] valid_0's l1: 1.72976e+06 [20] valid_0's l1: 1.72195e+06 [21] valid_0's l1: 1.72643e+06 [22] valid_0's l1: 1.72262e+06 [23] valid_0's l1: 1.7236e+06 [24] valid_0's l1: 1.72047e+06 [25] valid_0's l1: 1.72379e+06 [26] valid_0's l1: 1.72899e+06 [27] valid_0's l1: 1.72603e+06 [28] valid_0's l1: 1.72886e+06 [29] valid_0's l1: 1.72794e+06 [30] valid_0's l1: 1.72986e+06 [31] valid_0's l1: 1.73508e+06 [32] valid_0's l1: 1.73161e+06 [33] valid_0's l1: 1.73361e+06 [34] valid_0's l1: 1.72988e+06 [35] valid_0's l1: 1.73199e+06 [36] valid_0's l1: 1.73268e+06 [37] valid_0's l1: 1.73617e+06 [38] valid_0's l1: 1.73751e+06 [39] valid_0's l1: 1.73535e+06 [40] valid_0's l1: 1.73826e+06 [41] valid_0's l1: 1.74188e+06 [42] valid_0's l1: 1.74079e+06 [43] valid_0's l1: 1.74727e+06 [44] valid_0's l1: 1.74386e+06 [45] valid_0's l1: 1.7466e+06 [46] valid_0's l1: 1.74465e+06 [47] valid_0's l1: 1.74705e+06 [48] valid_0's l1: 1.74613e+06 [49] valid_0's l1: 1.74901e+06 [50] valid_0's l1: 1.74705e+06 [51] valid_0's l1: 1.74809e+06 [52] valid_0's l1: 1.7492e+06 [53] valid_0's l1: 1.75047e+06 [54] valid_0's l1: 1.7508e+06 Early stopping, best iteration is: [24] valid_0's l1: 1.72047e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.72212e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.16752e+06 [3] valid_0's l1: 2.74658e+06 [4] valid_0's l1: 2.44054e+06 [5] valid_0's l1: 2.22549e+06 [6] valid_0's l1: 2.14158e+06 [7] valid_0's l1: 2.08181e+06 [8] valid_0's l1: 1.97017e+06 [9] valid_0's l1: 1.89011e+06 [10] valid_0's l1: 1.83723e+06 [11] valid_0's l1: 1.78618e+06 [12] valid_0's l1: 1.77151e+06 [13] valid_0's l1: 1.74297e+06 [14] valid_0's l1: 1.74896e+06 [15] valid_0's l1: 1.73828e+06 [16] valid_0's l1: 1.72576e+06 [17] valid_0's l1: 1.71663e+06 [18] valid_0's l1: 1.71738e+06 [19] valid_0's l1: 1.71398e+06 [20] valid_0's l1: 1.70916e+06 [21] valid_0's l1: 1.7073e+06 [22] valid_0's l1: 1.71162e+06 [23] valid_0's l1: 1.71532e+06 [24] valid_0's l1: 1.71218e+06 [25] valid_0's l1: 1.71308e+06 [26] valid_0's l1: 1.71093e+06 [27] valid_0's l1: 1.71039e+06 [28] valid_0's l1: 1.71325e+06 [29] valid_0's l1: 1.71475e+06 [30] valid_0's l1: 1.71906e+06 [31] valid_0's l1: 1.72529e+06 [32] valid_0's l1: 1.7273e+06 [33] valid_0's l1: 1.72672e+06 [34] valid_0's l1: 1.72911e+06 [35] valid_0's l1: 1.73121e+06 [36] valid_0's l1: 1.72691e+06 [37] valid_0's l1: 1.72494e+06 [38] valid_0's l1: 1.72654e+06 [39] valid_0's l1: 1.72893e+06 [40] valid_0's l1: 1.72876e+06 [41] valid_0's l1: 1.72963e+06 [42] valid_0's l1: 1.73378e+06 [43] valid_0's l1: 1.73461e+06 [44] valid_0's l1: 1.73571e+06 [45] valid_0's l1: 1.73816e+06 [46] valid_0's l1: 1.74112e+06 [47] valid_0's l1: 1.74109e+06 [48] valid_0's l1: 1.74229e+06 [49] valid_0's l1: 1.74099e+06 [50] valid_0's l1: 1.74137e+06 [51] valid_0's l1: 1.74264e+06 Early stopping, best iteration is: [21] valid_0's l1: 1.7073e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.69405e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.1598e+06 [3] valid_0's l1: 2.76355e+06 [4] valid_0's l1: 2.45909e+06 [5] valid_0's l1: 2.24837e+06 [6] valid_0's l1: 2.17429e+06 [7] valid_0's l1: 2.11153e+06 [8] valid_0's l1: 1.98392e+06 [9] valid_0's l1: 1.89989e+06 [10] valid_0's l1: 1.8382e+06 [11] valid_0's l1: 1.78986e+06 [12] valid_0's l1: 1.77362e+06 [13] valid_0's l1: 1.74998e+06 [14] valid_0's l1: 1.7455e+06 [15] valid_0's l1: 1.72846e+06 [16] valid_0's l1: 1.71675e+06 [17] valid_0's l1: 1.71e+06 [18] valid_0's l1: 1.70856e+06 [19] valid_0's l1: 1.70935e+06 [20] valid_0's l1: 1.70615e+06 [21] valid_0's l1: 1.71159e+06 [22] valid_0's l1: 1.71179e+06 [23] valid_0's l1: 1.71527e+06 [24] valid_0's l1: 1.72176e+06 [25] valid_0's l1: 1.72737e+06 [26] valid_0's l1: 1.72886e+06 [27] valid_0's l1: 1.7298e+06 [28] valid_0's l1: 1.73337e+06 [29] valid_0's l1: 1.73864e+06 [30] valid_0's l1: 1.73277e+06 [31] valid_0's l1: 1.74082e+06 [32] valid_0's l1: 1.74536e+06 [33] valid_0's l1: 1.7446e+06 [34] valid_0's l1: 1.74336e+06 [35] valid_0's l1: 1.74879e+06 [36] valid_0's l1: 1.74662e+06 [37] valid_0's l1: 1.75018e+06 [38] valid_0's l1: 1.75158e+06 [39] valid_0's l1: 1.75496e+06 [40] valid_0's l1: 1.75484e+06 [41] valid_0's l1: 1.75695e+06 [42] valid_0's l1: 1.75627e+06 [43] valid_0's l1: 1.75882e+06 [44] valid_0's l1: 1.76028e+06 [45] valid_0's l1: 1.7591e+06 [46] valid_0's l1: 1.75985e+06 [47] valid_0's l1: 1.76132e+06 [48] valid_0's l1: 1.76081e+06 [49] valid_0's l1: 1.75987e+06 [50] valid_0's l1: 1.76197e+06 Early stopping, best iteration is: [20] valid_0's l1: 1.70615e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.65312e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.14754e+06 [3] valid_0's l1: 2.74253e+06 [4] valid_0's l1: 2.44702e+06 [5] valid_0's l1: 2.23118e+06 [6] valid_0's l1: 2.1436e+06 [7] valid_0's l1: 2.07788e+06 [8] valid_0's l1: 1.96328e+06 [9] valid_0's l1: 1.88086e+06 [10] valid_0's l1: 1.81434e+06 [11] valid_0's l1: 1.77025e+06 [12] valid_0's l1: 1.7582e+06 [13] valid_0's l1: 1.7324e+06 [14] valid_0's l1: 1.7307e+06 [15] valid_0's l1: 1.71914e+06 [16] valid_0's l1: 1.70847e+06 [17] valid_0's l1: 1.71082e+06 [18] valid_0's l1: 1.70652e+06 [19] valid_0's l1: 1.70882e+06 [20] valid_0's l1: 1.70653e+06 [21] valid_0's l1: 1.71105e+06 [22] valid_0's l1: 1.71115e+06 [23] valid_0's l1: 1.71315e+06 [24] valid_0's l1: 1.71157e+06 [25] valid_0's l1: 1.72015e+06 [26] valid_0's l1: 1.72486e+06 [27] valid_0's l1: 1.72282e+06 [28] valid_0's l1: 1.72413e+06 [29] valid_0's l1: 1.72733e+06 [30] valid_0's l1: 1.72706e+06 [31] valid_0's l1: 1.72835e+06 [32] valid_0's l1: 1.72733e+06 [33] valid_0's l1: 1.73047e+06 [34] valid_0's l1: 1.72756e+06 [35] valid_0's l1: 1.72728e+06 [36] valid_0's l1: 1.73103e+06 [37] valid_0's l1: 1.73193e+06 [38] valid_0's l1: 1.73618e+06 [39] valid_0's l1: 1.73471e+06 [40] valid_0's l1: 1.73887e+06 [41] valid_0's l1: 1.73759e+06 [42] valid_0's l1: 1.74282e+06 [43] valid_0's l1: 1.74739e+06 [44] valid_0's l1: 1.74498e+06 [45] valid_0's l1: 1.74684e+06 [46] valid_0's l1: 1.75015e+06 [47] valid_0's l1: 1.7487e+06 [48] valid_0's l1: 1.74989e+06 Early stopping, best iteration is: [18] valid_0's l1: 1.70652e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.72204e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.17054e+06 [3] valid_0's l1: 2.74648e+06 [4] valid_0's l1: 2.44099e+06 [5] valid_0's l1: 2.22643e+06 [6] valid_0's l1: 2.13949e+06 [7] valid_0's l1: 2.07147e+06 [8] valid_0's l1: 1.96228e+06 [9] valid_0's l1: 1.88236e+06 [10] valid_0's l1: 1.82206e+06 [11] valid_0's l1: 1.7695e+06 [12] valid_0's l1: 1.75469e+06 [13] valid_0's l1: 1.72994e+06 [14] valid_0's l1: 1.73225e+06 [15] valid_0's l1: 1.71957e+06 [16] valid_0's l1: 1.70141e+06 [17] valid_0's l1: 1.68815e+06 [18] valid_0's l1: 1.69655e+06 [19] valid_0's l1: 1.69964e+06 [20] valid_0's l1: 1.6935e+06 [21] valid_0's l1: 1.6906e+06 [22] valid_0's l1: 1.69327e+06 [23] valid_0's l1: 1.69228e+06 [24] valid_0's l1: 1.69521e+06 [25] valid_0's l1: 1.69694e+06 [26] valid_0's l1: 1.69293e+06 [27] valid_0's l1: 1.69643e+06 [28] valid_0's l1: 1.69572e+06 [29] valid_0's l1: 1.69559e+06 [30] valid_0's l1: 1.69489e+06 [31] valid_0's l1: 1.70033e+06 [32] valid_0's l1: 1.70343e+06 [33] valid_0's l1: 1.70433e+06 [34] valid_0's l1: 1.70409e+06 [35] valid_0's l1: 1.70692e+06 [36] valid_0's l1: 1.70936e+06 [37] valid_0's l1: 1.71489e+06 [38] valid_0's l1: 1.71212e+06 [39] valid_0's l1: 1.71647e+06 [40] valid_0's l1: 1.71969e+06 [41] valid_0's l1: 1.72057e+06 [42] valid_0's l1: 1.72345e+06 [43] valid_0's l1: 1.72367e+06 [44] valid_0's l1: 1.72635e+06 [45] valid_0's l1: 1.73191e+06 [46] valid_0's l1: 1.7332e+06 [47] valid_0's l1: 1.73282e+06 Early stopping, best iteration is: [17] valid_0's l1: 1.68815e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.69457e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.16036e+06 [3] valid_0's l1: 2.76039e+06 [4] valid_0's l1: 2.46011e+06 [5] valid_0's l1: 2.25014e+06 [6] valid_0's l1: 2.17619e+06 [7] valid_0's l1: 2.12057e+06 [8] valid_0's l1: 1.99046e+06 [9] valid_0's l1: 1.90537e+06 [10] valid_0's l1: 1.84246e+06 [11] valid_0's l1: 1.79557e+06 [12] valid_0's l1: 1.77552e+06 [13] valid_0's l1: 1.7552e+06 [14] valid_0's l1: 1.75783e+06 [15] valid_0's l1: 1.74009e+06 [16] valid_0's l1: 1.72329e+06 [17] valid_0's l1: 1.71541e+06 [18] valid_0's l1: 1.71353e+06 [19] valid_0's l1: 1.71455e+06 [20] valid_0's l1: 1.70729e+06 [21] valid_0's l1: 1.71074e+06 [22] valid_0's l1: 1.71576e+06 [23] valid_0's l1: 1.71677e+06 [24] valid_0's l1: 1.72341e+06 [25] valid_0's l1: 1.72883e+06 [26] valid_0's l1: 1.72534e+06 [27] valid_0's l1: 1.72912e+06 [28] valid_0's l1: 1.73368e+06 [29] valid_0's l1: 1.73878e+06 [30] valid_0's l1: 1.73599e+06 [31] valid_0's l1: 1.73795e+06 [32] valid_0's l1: 1.74221e+06 [33] valid_0's l1: 1.74634e+06 [34] valid_0's l1: 1.74628e+06 [35] valid_0's l1: 1.75163e+06 [36] valid_0's l1: 1.75402e+06 [37] valid_0's l1: 1.75099e+06 [38] valid_0's l1: 1.75159e+06 [39] valid_0's l1: 1.75533e+06 [40] valid_0's l1: 1.75661e+06 [41] valid_0's l1: 1.7594e+06 [42] valid_0's l1: 1.75635e+06 [43] valid_0's l1: 1.75724e+06 [44] valid_0's l1: 1.7619e+06 [45] valid_0's l1: 1.7619e+06 [46] valid_0's l1: 1.76091e+06 [47] valid_0's l1: 1.76298e+06 [48] valid_0's l1: 1.76366e+06 [49] valid_0's l1: 1.76831e+06 [50] valid_0's l1: 1.77015e+06 Early stopping, best iteration is: [20] valid_0's l1: 1.70729e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.65467e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.14936e+06 [3] valid_0's l1: 2.7456e+06 [4] valid_0's l1: 2.44883e+06 [5] valid_0's l1: 2.23091e+06 [6] valid_0's l1: 2.13898e+06 [7] valid_0's l1: 2.07805e+06 [8] valid_0's l1: 1.97044e+06 [9] valid_0's l1: 1.89433e+06 [10] valid_0's l1: 1.8338e+06 [11] valid_0's l1: 1.79255e+06 [12] valid_0's l1: 1.78759e+06 [13] valid_0's l1: 1.75872e+06 [14] valid_0's l1: 1.75191e+06 [15] valid_0's l1: 1.74271e+06 [16] valid_0's l1: 1.73072e+06 [17] valid_0's l1: 1.72693e+06 [18] valid_0's l1: 1.72805e+06 [19] valid_0's l1: 1.72976e+06 [20] valid_0's l1: 1.72195e+06 [21] valid_0's l1: 1.72643e+06 [22] valid_0's l1: 1.72262e+06 [23] valid_0's l1: 1.7236e+06 [24] valid_0's l1: 1.72047e+06 [25] valid_0's l1: 1.72379e+06 [26] valid_0's l1: 1.72899e+06 [27] valid_0's l1: 1.72603e+06 [28] valid_0's l1: 1.72886e+06 [29] valid_0's l1: 1.72794e+06 [30] valid_0's l1: 1.72986e+06 [31] valid_0's l1: 1.73508e+06 [32] valid_0's l1: 1.73161e+06 [33] valid_0's l1: 1.73361e+06 [34] valid_0's l1: 1.72988e+06 [35] valid_0's l1: 1.73199e+06 [36] valid_0's l1: 1.73268e+06 [37] valid_0's l1: 1.73617e+06 [38] valid_0's l1: 1.73751e+06 [39] valid_0's l1: 1.73535e+06 [40] valid_0's l1: 1.73826e+06 [41] valid_0's l1: 1.74188e+06 [42] valid_0's l1: 1.74079e+06 [43] valid_0's l1: 1.74727e+06 [44] valid_0's l1: 1.74386e+06 [45] valid_0's l1: 1.7466e+06 [46] valid_0's l1: 1.74465e+06 [47] valid_0's l1: 1.74705e+06 [48] valid_0's l1: 1.74613e+06 [49] valid_0's l1: 1.74901e+06 [50] valid_0's l1: 1.74705e+06 [51] valid_0's l1: 1.74809e+06 [52] valid_0's l1: 1.7492e+06 [53] valid_0's l1: 1.75047e+06 [54] valid_0's l1: 1.7508e+06 Early stopping, best iteration is: [24] valid_0's l1: 1.72047e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.72212e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.16752e+06 [3] valid_0's l1: 2.74658e+06 [4] valid_0's l1: 2.44054e+06 [5] valid_0's l1: 2.22549e+06 [6] valid_0's l1: 2.14158e+06 [7] valid_0's l1: 2.08181e+06 [8] valid_0's l1: 1.97017e+06 [9] valid_0's l1: 1.89011e+06 [10] valid_0's l1: 1.83723e+06 [11] valid_0's l1: 1.78618e+06 [12] valid_0's l1: 1.77151e+06 [13] valid_0's l1: 1.74297e+06 [14] valid_0's l1: 1.74896e+06 [15] valid_0's l1: 1.73828e+06 [16] valid_0's l1: 1.72576e+06 [17] valid_0's l1: 1.71663e+06 [18] valid_0's l1: 1.71738e+06 [19] valid_0's l1: 1.71398e+06 [20] valid_0's l1: 1.70916e+06 [21] valid_0's l1: 1.7073e+06 [22] valid_0's l1: 1.71162e+06 [23] valid_0's l1: 1.71532e+06 [24] valid_0's l1: 1.71218e+06 [25] valid_0's l1: 1.71308e+06 [26] valid_0's l1: 1.71093e+06 [27] valid_0's l1: 1.71039e+06 [28] valid_0's l1: 1.71325e+06 [29] valid_0's l1: 1.71475e+06 [30] valid_0's l1: 1.71906e+06 [31] valid_0's l1: 1.72529e+06 [32] valid_0's l1: 1.7273e+06 [33] valid_0's l1: 1.72672e+06 [34] valid_0's l1: 1.72911e+06 [35] valid_0's l1: 1.73121e+06 [36] valid_0's l1: 1.72691e+06 [37] valid_0's l1: 1.72494e+06 [38] valid_0's l1: 1.72654e+06 [39] valid_0's l1: 1.72893e+06 [40] valid_0's l1: 1.72876e+06 [41] valid_0's l1: 1.72963e+06 [42] valid_0's l1: 1.73378e+06 [43] valid_0's l1: 1.73461e+06 [44] valid_0's l1: 1.73571e+06 [45] valid_0's l1: 1.73816e+06 [46] valid_0's l1: 1.74112e+06 [47] valid_0's l1: 1.74109e+06 [48] valid_0's l1: 1.74229e+06 [49] valid_0's l1: 1.74099e+06 [50] valid_0's l1: 1.74137e+06 [51] valid_0's l1: 1.74264e+06 Early stopping, best iteration is: [21] valid_0's l1: 1.7073e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.7301e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.1745e+06 [3] valid_0's l1: 2.77688e+06 [4] valid_0's l1: 2.47445e+06 [5] valid_0's l1: 2.26323e+06 [6] valid_0's l1: 2.20126e+06 [7] valid_0's l1: 2.15119e+06 [8] valid_0's l1: 2.01153e+06 [9] valid_0's l1: 1.90874e+06 [10] valid_0's l1: 1.84571e+06 [11] valid_0's l1: 1.78861e+06 [12] valid_0's l1: 1.77501e+06 [13] valid_0's l1: 1.7415e+06 [14] valid_0's l1: 1.73695e+06 [15] valid_0's l1: 1.71664e+06 [16] valid_0's l1: 1.70057e+06 [17] valid_0's l1: 1.68575e+06 [18] valid_0's l1: 1.68101e+06 [19] valid_0's l1: 1.66905e+06 [20] valid_0's l1: 1.66859e+06 [21] valid_0's l1: 1.67162e+06 [22] valid_0's l1: 1.6639e+06 [23] valid_0's l1: 1.66392e+06 [24] valid_0's l1: 1.65765e+06 [25] valid_0's l1: 1.65855e+06 [26] valid_0's l1: 1.66059e+06 [27] valid_0's l1: 1.65435e+06 [28] valid_0's l1: 1.65028e+06 [29] valid_0's l1: 1.64869e+06 [30] valid_0's l1: 1.64393e+06 [31] valid_0's l1: 1.64521e+06 [32] valid_0's l1: 1.6455e+06 [33] valid_0's l1: 1.64542e+06 [34] valid_0's l1: 1.64099e+06 [35] valid_0's l1: 1.64512e+06 [36] valid_0's l1: 1.64712e+06 [37] valid_0's l1: 1.64712e+06 [38] valid_0's l1: 1.64563e+06 [39] valid_0's l1: 1.64968e+06 [40] valid_0's l1: 1.65098e+06 [41] valid_0's l1: 1.64928e+06 [42] valid_0's l1: 1.64421e+06 [43] valid_0's l1: 1.64426e+06 [44] valid_0's l1: 1.64907e+06 [45] valid_0's l1: 1.6469e+06 [46] valid_0's l1: 1.64921e+06 [47] valid_0's l1: 1.65081e+06 [48] valid_0's l1: 1.65122e+06 [49] valid_0's l1: 1.65125e+06 [50] valid_0's l1: 1.65428e+06 [51] valid_0's l1: 1.65118e+06 [52] valid_0's l1: 1.6508e+06 [53] valid_0's l1: 1.65457e+06 [54] valid_0's l1: 1.65743e+06 [55] valid_0's l1: 1.65928e+06 [56] valid_0's l1: 1.65673e+06 [57] valid_0's l1: 1.65866e+06 [58] valid_0's l1: 1.66043e+06 [59] valid_0's l1: 1.65911e+06 [60] valid_0's l1: 1.65978e+06 [61] valid_0's l1: 1.66008e+06 [62] valid_0's l1: 1.65961e+06 [63] valid_0's l1: 1.65945e+06 [64] valid_0's l1: 1.66602e+06 Early stopping, best iteration is: [34] valid_0's l1: 1.64099e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.68661e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.1591e+06 [3] valid_0's l1: 2.75499e+06 [4] valid_0's l1: 2.45416e+06 [5] valid_0's l1: 2.23723e+06 [6] valid_0's l1: 2.1592e+06 [7] valid_0's l1: 2.10718e+06 [8] valid_0's l1: 1.98413e+06 [9] valid_0's l1: 1.89977e+06 [10] valid_0's l1: 1.83449e+06 [11] valid_0's l1: 1.78458e+06 [12] valid_0's l1: 1.76969e+06 [13] valid_0's l1: 1.73441e+06 [14] valid_0's l1: 1.73006e+06 [15] valid_0's l1: 1.7097e+06 [16] valid_0's l1: 1.69813e+06 [17] valid_0's l1: 1.69385e+06 [18] valid_0's l1: 1.67542e+06 [19] valid_0's l1: 1.66869e+06 [20] valid_0's l1: 1.66645e+06 [21] valid_0's l1: 1.66198e+06 [22] valid_0's l1: 1.663e+06 [23] valid_0's l1: 1.65616e+06 [24] valid_0's l1: 1.65922e+06 [25] valid_0's l1: 1.65335e+06 [26] valid_0's l1: 1.6563e+06 [27] valid_0's l1: 1.65191e+06 [28] valid_0's l1: 1.65176e+06 [29] valid_0's l1: 1.6513e+06 [30] valid_0's l1: 1.64843e+06 [31] valid_0's l1: 1.65164e+06 [32] valid_0's l1: 1.64712e+06 [33] valid_0's l1: 1.64766e+06 [34] valid_0's l1: 1.6514e+06 [35] valid_0's l1: 1.65158e+06 [36] valid_0's l1: 1.65444e+06 [37] valid_0's l1: 1.6581e+06 [38] valid_0's l1: 1.65599e+06 [39] valid_0's l1: 1.65938e+06 [40] valid_0's l1: 1.66228e+06 [41] valid_0's l1: 1.66307e+06 [42] valid_0's l1: 1.66281e+06 [43] valid_0's l1: 1.66354e+06 [44] valid_0's l1: 1.6595e+06 [45] valid_0's l1: 1.66067e+06 [46] valid_0's l1: 1.65911e+06 [47] valid_0's l1: 1.66115e+06 [48] valid_0's l1: 1.65837e+06 [49] valid_0's l1: 1.65968e+06 [50] valid_0's l1: 1.65673e+06 [51] valid_0's l1: 1.65973e+06 [52] valid_0's l1: 1.6606e+06 [53] valid_0's l1: 1.6596e+06 [54] valid_0's l1: 1.66112e+06 [55] valid_0's l1: 1.65963e+06 [56] valid_0's l1: 1.66089e+06 [57] valid_0's l1: 1.65919e+06 [58] valid_0's l1: 1.6567e+06 [59] valid_0's l1: 1.65635e+06 [60] valid_0's l1: 1.65687e+06 [61] valid_0's l1: 1.65557e+06 [62] valid_0's l1: 1.65768e+06 Early stopping, best iteration is: [32] valid_0's l1: 1.64712e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.75296e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.19259e+06 [3] valid_0's l1: 2.7636e+06 [4] valid_0's l1: 2.45106e+06 [5] valid_0's l1: 2.23614e+06 [6] valid_0's l1: 2.17392e+06 [7] valid_0's l1: 2.12559e+06 [8] valid_0's l1: 1.99899e+06 [9] valid_0's l1: 1.9085e+06 [10] valid_0's l1: 1.83877e+06 [11] valid_0's l1: 1.78734e+06 [12] valid_0's l1: 1.75899e+06 [13] valid_0's l1: 1.71648e+06 [14] valid_0's l1: 1.71539e+06 [15] valid_0's l1: 1.68911e+06 [16] valid_0's l1: 1.67195e+06 [17] valid_0's l1: 1.65462e+06 [18] valid_0's l1: 1.65416e+06 [19] valid_0's l1: 1.65014e+06 [20] valid_0's l1: 1.64412e+06 [21] valid_0's l1: 1.6371e+06 [22] valid_0's l1: 1.63928e+06 [23] valid_0's l1: 1.63282e+06 [24] valid_0's l1: 1.63717e+06 [25] valid_0's l1: 1.63757e+06 [26] valid_0's l1: 1.63509e+06 [27] valid_0's l1: 1.63896e+06 [28] valid_0's l1: 1.63966e+06 [29] valid_0's l1: 1.6405e+06 [30] valid_0's l1: 1.64241e+06 [31] valid_0's l1: 1.64361e+06 [32] valid_0's l1: 1.64613e+06 [33] valid_0's l1: 1.64798e+06 [34] valid_0's l1: 1.64872e+06 [35] valid_0's l1: 1.64828e+06 [36] valid_0's l1: 1.64506e+06 [37] valid_0's l1: 1.64929e+06 [38] valid_0's l1: 1.64905e+06 [39] valid_0's l1: 1.65143e+06 [40] valid_0's l1: 1.64509e+06 [41] valid_0's l1: 1.64443e+06 [42] valid_0's l1: 1.64504e+06 [43] valid_0's l1: 1.64629e+06 [44] valid_0's l1: 1.64587e+06 [45] valid_0's l1: 1.64659e+06 [46] valid_0's l1: 1.6472e+06 [47] valid_0's l1: 1.65008e+06 [48] valid_0's l1: 1.65641e+06 [49] valid_0's l1: 1.65651e+06 [50] valid_0's l1: 1.65755e+06 [51] valid_0's l1: 1.65789e+06 [52] valid_0's l1: 1.65927e+06 [53] valid_0's l1: 1.65914e+06 Early stopping, best iteration is: [23] valid_0's l1: 1.63282e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.7301e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.1745e+06 [3] valid_0's l1: 2.77688e+06 [4] valid_0's l1: 2.47445e+06 [5] valid_0's l1: 2.26323e+06 [6] valid_0's l1: 2.20126e+06 [7] valid_0's l1: 2.15119e+06 [8] valid_0's l1: 2.01153e+06 [9] valid_0's l1: 1.90874e+06 [10] valid_0's l1: 1.84571e+06 [11] valid_0's l1: 1.78861e+06 [12] valid_0's l1: 1.77501e+06 [13] valid_0's l1: 1.7415e+06 [14] valid_0's l1: 1.73695e+06 [15] valid_0's l1: 1.71664e+06 [16] valid_0's l1: 1.70057e+06 [17] valid_0's l1: 1.68575e+06 [18] valid_0's l1: 1.68101e+06 [19] valid_0's l1: 1.66905e+06 [20] valid_0's l1: 1.66859e+06 [21] valid_0's l1: 1.67162e+06 [22] valid_0's l1: 1.6639e+06 [23] valid_0's l1: 1.66392e+06 [24] valid_0's l1: 1.65765e+06 [25] valid_0's l1: 1.65855e+06 [26] valid_0's l1: 1.66059e+06 [27] valid_0's l1: 1.65435e+06 [28] valid_0's l1: 1.65028e+06 [29] valid_0's l1: 1.64869e+06 [30] valid_0's l1: 1.64393e+06 [31] valid_0's l1: 1.64521e+06 [32] valid_0's l1: 1.6455e+06 [33] valid_0's l1: 1.64542e+06 [34] valid_0's l1: 1.64099e+06 [35] valid_0's l1: 1.64512e+06 [36] valid_0's l1: 1.64712e+06 [37] valid_0's l1: 1.64712e+06 [38] valid_0's l1: 1.64563e+06 [39] valid_0's l1: 1.64968e+06 [40] valid_0's l1: 1.65098e+06 [41] valid_0's l1: 1.64928e+06 [42] valid_0's l1: 1.64421e+06 [43] valid_0's l1: 1.64426e+06 [44] valid_0's l1: 1.64907e+06 [45] valid_0's l1: 1.6469e+06 [46] valid_0's l1: 1.64921e+06 [47] valid_0's l1: 1.65081e+06 [48] valid_0's l1: 1.65122e+06 [49] valid_0's l1: 1.65125e+06 [50] valid_0's l1: 1.65428e+06 [51] valid_0's l1: 1.65118e+06 [52] valid_0's l1: 1.6508e+06 [53] valid_0's l1: 1.65457e+06 [54] valid_0's l1: 1.65743e+06 [55] valid_0's l1: 1.65928e+06 [56] valid_0's l1: 1.65673e+06 [57] valid_0's l1: 1.65866e+06 [58] valid_0's l1: 1.66043e+06 [59] valid_0's l1: 1.65911e+06 [60] valid_0's l1: 1.65978e+06 [61] valid_0's l1: 1.66008e+06 [62] valid_0's l1: 1.65961e+06 [63] valid_0's l1: 1.65945e+06 [64] valid_0's l1: 1.66602e+06 Early stopping, best iteration is: [34] valid_0's l1: 1.64099e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.68661e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.1591e+06 [3] valid_0's l1: 2.75499e+06 [4] valid_0's l1: 2.45416e+06 [5] valid_0's l1: 2.23723e+06 [6] valid_0's l1: 2.1592e+06 [7] valid_0's l1: 2.10718e+06 [8] valid_0's l1: 1.98413e+06 [9] valid_0's l1: 1.89977e+06 [10] valid_0's l1: 1.83449e+06 [11] valid_0's l1: 1.78458e+06 [12] valid_0's l1: 1.76969e+06 [13] valid_0's l1: 1.73441e+06 [14] valid_0's l1: 1.73006e+06 [15] valid_0's l1: 1.7097e+06 [16] valid_0's l1: 1.69813e+06 [17] valid_0's l1: 1.69385e+06 [18] valid_0's l1: 1.67542e+06 [19] valid_0's l1: 1.66869e+06 [20] valid_0's l1: 1.66645e+06 [21] valid_0's l1: 1.66198e+06 [22] valid_0's l1: 1.663e+06 [23] valid_0's l1: 1.65616e+06 [24] valid_0's l1: 1.65922e+06 [25] valid_0's l1: 1.65335e+06 [26] valid_0's l1: 1.6563e+06 [27] valid_0's l1: 1.65191e+06 [28] valid_0's l1: 1.65176e+06 [29] valid_0's l1: 1.6513e+06 [30] valid_0's l1: 1.64843e+06 [31] valid_0's l1: 1.65164e+06 [32] valid_0's l1: 1.64712e+06 [33] valid_0's l1: 1.64766e+06 [34] valid_0's l1: 1.6514e+06 [35] valid_0's l1: 1.65158e+06 [36] valid_0's l1: 1.65444e+06 [37] valid_0's l1: 1.6581e+06 [38] valid_0's l1: 1.65599e+06 [39] valid_0's l1: 1.65938e+06 [40] valid_0's l1: 1.66228e+06 [41] valid_0's l1: 1.66307e+06 [42] valid_0's l1: 1.66281e+06 [43] valid_0's l1: 1.66354e+06 [44] valid_0's l1: 1.6595e+06 [45] valid_0's l1: 1.66067e+06 [46] valid_0's l1: 1.65911e+06 [47] valid_0's l1: 1.66115e+06 [48] valid_0's l1: 1.65837e+06 [49] valid_0's l1: 1.65968e+06 [50] valid_0's l1: 1.65673e+06 [51] valid_0's l1: 1.65973e+06 [52] valid_0's l1: 1.6606e+06 [53] valid_0's l1: 1.6596e+06 [54] valid_0's l1: 1.66112e+06 [55] valid_0's l1: 1.65963e+06 [56] valid_0's l1: 1.66089e+06 [57] valid_0's l1: 1.65919e+06 [58] valid_0's l1: 1.6567e+06 [59] valid_0's l1: 1.65635e+06 [60] valid_0's l1: 1.65687e+06 [61] valid_0's l1: 1.65557e+06 [62] valid_0's l1: 1.65768e+06 Early stopping, best iteration is: [32] valid_0's l1: 1.64712e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.75296e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.19259e+06 [3] valid_0's l1: 2.7636e+06 [4] valid_0's l1: 2.45106e+06 [5] valid_0's l1: 2.23614e+06 [6] valid_0's l1: 2.17392e+06 [7] valid_0's l1: 2.12559e+06 [8] valid_0's l1: 1.99899e+06 [9] valid_0's l1: 1.9085e+06 [10] valid_0's l1: 1.83877e+06 [11] valid_0's l1: 1.78734e+06 [12] valid_0's l1: 1.75899e+06 [13] valid_0's l1: 1.71648e+06 [14] valid_0's l1: 1.71539e+06 [15] valid_0's l1: 1.68911e+06 [16] valid_0's l1: 1.67195e+06 [17] valid_0's l1: 1.65462e+06 [18] valid_0's l1: 1.65416e+06 [19] valid_0's l1: 1.65014e+06 [20] valid_0's l1: 1.64412e+06 [21] valid_0's l1: 1.6371e+06 [22] valid_0's l1: 1.63928e+06 [23] valid_0's l1: 1.63282e+06 [24] valid_0's l1: 1.63717e+06 [25] valid_0's l1: 1.63757e+06 [26] valid_0's l1: 1.63509e+06 [27] valid_0's l1: 1.63896e+06 [28] valid_0's l1: 1.63966e+06 [29] valid_0's l1: 1.6405e+06 [30] valid_0's l1: 1.64241e+06 [31] valid_0's l1: 1.64361e+06 [32] valid_0's l1: 1.64613e+06 [33] valid_0's l1: 1.64798e+06 [34] valid_0's l1: 1.64872e+06 [35] valid_0's l1: 1.64828e+06 [36] valid_0's l1: 1.64506e+06 [37] valid_0's l1: 1.64929e+06 [38] valid_0's l1: 1.64905e+06 [39] valid_0's l1: 1.65143e+06 [40] valid_0's l1: 1.64509e+06 [41] valid_0's l1: 1.64443e+06 [42] valid_0's l1: 1.64504e+06 [43] valid_0's l1: 1.64629e+06 [44] valid_0's l1: 1.64587e+06 [45] valid_0's l1: 1.64659e+06 [46] valid_0's l1: 1.6472e+06 [47] valid_0's l1: 1.65008e+06 [48] valid_0's l1: 1.65641e+06 [49] valid_0's l1: 1.65651e+06 [50] valid_0's l1: 1.65755e+06 [51] valid_0's l1: 1.65789e+06 [52] valid_0's l1: 1.65927e+06 [53] valid_0's l1: 1.65914e+06 Early stopping, best iteration is: [23] valid_0's l1: 1.63282e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.7301e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.1745e+06 [3] valid_0's l1: 2.77688e+06 [4] valid_0's l1: 2.47445e+06 [5] valid_0's l1: 2.26323e+06 [6] valid_0's l1: 2.20126e+06 [7] valid_0's l1: 2.15119e+06 [8] valid_0's l1: 2.01153e+06 [9] valid_0's l1: 1.90874e+06 [10] valid_0's l1: 1.84571e+06 [11] valid_0's l1: 1.78861e+06 [12] valid_0's l1: 1.77501e+06 [13] valid_0's l1: 1.7415e+06 [14] valid_0's l1: 1.73695e+06 [15] valid_0's l1: 1.71664e+06 [16] valid_0's l1: 1.70057e+06 [17] valid_0's l1: 1.68575e+06 [18] valid_0's l1: 1.68101e+06 [19] valid_0's l1: 1.66905e+06 [20] valid_0's l1: 1.66859e+06 [21] valid_0's l1: 1.67162e+06 [22] valid_0's l1: 1.6639e+06 [23] valid_0's l1: 1.66392e+06 [24] valid_0's l1: 1.65765e+06 [25] valid_0's l1: 1.65855e+06 [26] valid_0's l1: 1.66059e+06 [27] valid_0's l1: 1.65435e+06 [28] valid_0's l1: 1.65028e+06 [29] valid_0's l1: 1.64869e+06 [30] valid_0's l1: 1.64393e+06 [31] valid_0's l1: 1.64521e+06 [32] valid_0's l1: 1.6455e+06 [33] valid_0's l1: 1.64542e+06 [34] valid_0's l1: 1.64099e+06 [35] valid_0's l1: 1.64512e+06 [36] valid_0's l1: 1.64712e+06 [37] valid_0's l1: 1.64712e+06 [38] valid_0's l1: 1.64563e+06 [39] valid_0's l1: 1.64968e+06 [40] valid_0's l1: 1.65098e+06 [41] valid_0's l1: 1.64928e+06 [42] valid_0's l1: 1.64421e+06 [43] valid_0's l1: 1.64426e+06 [44] valid_0's l1: 1.64907e+06 [45] valid_0's l1: 1.6469e+06 [46] valid_0's l1: 1.64921e+06 [47] valid_0's l1: 1.65081e+06 [48] valid_0's l1: 1.65122e+06 [49] valid_0's l1: 1.65125e+06 [50] valid_0's l1: 1.65428e+06 [51] valid_0's l1: 1.65118e+06 [52] valid_0's l1: 1.6508e+06 [53] valid_0's l1: 1.65457e+06 [54] valid_0's l1: 1.65743e+06 [55] valid_0's l1: 1.65928e+06 [56] valid_0's l1: 1.65673e+06 [57] valid_0's l1: 1.65866e+06 [58] valid_0's l1: 1.66043e+06 [59] valid_0's l1: 1.65911e+06 [60] valid_0's l1: 1.65978e+06 [61] valid_0's l1: 1.66008e+06 [62] valid_0's l1: 1.65961e+06 [63] valid_0's l1: 1.65945e+06 [64] valid_0's l1: 1.66602e+06 Early stopping, best iteration is: [34] valid_0's l1: 1.64099e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.68661e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.1591e+06 [3] valid_0's l1: 2.75499e+06 [4] valid_0's l1: 2.45416e+06 [5] valid_0's l1: 2.23723e+06 [6] valid_0's l1: 2.1592e+06 [7] valid_0's l1: 2.10718e+06 [8] valid_0's l1: 1.98413e+06 [9] valid_0's l1: 1.89977e+06 [10] valid_0's l1: 1.83449e+06 [11] valid_0's l1: 1.78458e+06 [12] valid_0's l1: 1.76969e+06 [13] valid_0's l1: 1.73441e+06 [14] valid_0's l1: 1.73006e+06 [15] valid_0's l1: 1.7097e+06 [16] valid_0's l1: 1.69813e+06 [17] valid_0's l1: 1.69385e+06 [18] valid_0's l1: 1.67542e+06 [19] valid_0's l1: 1.66869e+06 [20] valid_0's l1: 1.66645e+06 [21] valid_0's l1: 1.66198e+06 [22] valid_0's l1: 1.663e+06 [23] valid_0's l1: 1.65616e+06 [24] valid_0's l1: 1.65922e+06 [25] valid_0's l1: 1.65335e+06 [26] valid_0's l1: 1.6563e+06 [27] valid_0's l1: 1.65191e+06 [28] valid_0's l1: 1.65176e+06 [29] valid_0's l1: 1.6513e+06 [30] valid_0's l1: 1.64843e+06 [31] valid_0's l1: 1.65164e+06 [32] valid_0's l1: 1.64712e+06 [33] valid_0's l1: 1.64766e+06 [34] valid_0's l1: 1.6514e+06 [35] valid_0's l1: 1.65158e+06 [36] valid_0's l1: 1.65444e+06 [37] valid_0's l1: 1.6581e+06 [38] valid_0's l1: 1.65599e+06 [39] valid_0's l1: 1.65938e+06 [40] valid_0's l1: 1.66228e+06 [41] valid_0's l1: 1.66307e+06 [42] valid_0's l1: 1.66281e+06 [43] valid_0's l1: 1.66354e+06 [44] valid_0's l1: 1.6595e+06 [45] valid_0's l1: 1.66067e+06 [46] valid_0's l1: 1.65911e+06 [47] valid_0's l1: 1.66115e+06 [48] valid_0's l1: 1.65837e+06 [49] valid_0's l1: 1.65968e+06 [50] valid_0's l1: 1.65673e+06 [51] valid_0's l1: 1.65973e+06 [52] valid_0's l1: 1.6606e+06 [53] valid_0's l1: 1.6596e+06 [54] valid_0's l1: 1.66112e+06 [55] valid_0's l1: 1.65963e+06 [56] valid_0's l1: 1.66089e+06 [57] valid_0's l1: 1.65919e+06 [58] valid_0's l1: 1.6567e+06 [59] valid_0's l1: 1.65635e+06 [60] valid_0's l1: 1.65687e+06 [61] valid_0's l1: 1.65557e+06 [62] valid_0's l1: 1.65768e+06 Early stopping, best iteration is: [32] valid_0's l1: 1.64712e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.75296e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.19259e+06 [3] valid_0's l1: 2.7636e+06 [4] valid_0's l1: 2.45106e+06 [5] valid_0's l1: 2.23614e+06 [6] valid_0's l1: 2.17392e+06 [7] valid_0's l1: 2.12559e+06 [8] valid_0's l1: 1.99899e+06 [9] valid_0's l1: 1.9085e+06 [10] valid_0's l1: 1.83877e+06 [11] valid_0's l1: 1.78734e+06 [12] valid_0's l1: 1.75899e+06 [13] valid_0's l1: 1.71648e+06 [14] valid_0's l1: 1.71539e+06 [15] valid_0's l1: 1.68911e+06 [16] valid_0's l1: 1.67195e+06 [17] valid_0's l1: 1.65462e+06 [18] valid_0's l1: 1.65416e+06 [19] valid_0's l1: 1.65014e+06 [20] valid_0's l1: 1.64412e+06 [21] valid_0's l1: 1.6371e+06 [22] valid_0's l1: 1.63928e+06 [23] valid_0's l1: 1.63282e+06 [24] valid_0's l1: 1.63717e+06 [25] valid_0's l1: 1.63757e+06 [26] valid_0's l1: 1.63509e+06 [27] valid_0's l1: 1.63896e+06 [28] valid_0's l1: 1.63966e+06 [29] valid_0's l1: 1.6405e+06 [30] valid_0's l1: 1.64241e+06 [31] valid_0's l1: 1.64361e+06 [32] valid_0's l1: 1.64613e+06 [33] valid_0's l1: 1.64798e+06 [34] valid_0's l1: 1.64872e+06 [35] valid_0's l1: 1.64828e+06 [36] valid_0's l1: 1.64506e+06 [37] valid_0's l1: 1.64929e+06 [38] valid_0's l1: 1.64905e+06 [39] valid_0's l1: 1.65143e+06 [40] valid_0's l1: 1.64509e+06 [41] valid_0's l1: 1.64443e+06 [42] valid_0's l1: 1.64504e+06 [43] valid_0's l1: 1.64629e+06 [44] valid_0's l1: 1.64587e+06 [45] valid_0's l1: 1.64659e+06 [46] valid_0's l1: 1.6472e+06 [47] valid_0's l1: 1.65008e+06 [48] valid_0's l1: 1.65641e+06 [49] valid_0's l1: 1.65651e+06 [50] valid_0's l1: 1.65755e+06 [51] valid_0's l1: 1.65789e+06 [52] valid_0's l1: 1.65927e+06 [53] valid_0's l1: 1.65914e+06 Early stopping, best iteration is: [23] valid_0's l1: 1.63282e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.7301e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.1745e+06 [3] valid_0's l1: 2.77688e+06 [4] valid_0's l1: 2.47445e+06 [5] valid_0's l1: 2.26323e+06 [6] valid_0's l1: 2.20126e+06 [7] valid_0's l1: 2.15119e+06 [8] valid_0's l1: 2.01153e+06 [9] valid_0's l1: 1.90874e+06 [10] valid_0's l1: 1.84571e+06 [11] valid_0's l1: 1.78861e+06 [12] valid_0's l1: 1.77501e+06 [13] valid_0's l1: 1.7415e+06 [14] valid_0's l1: 1.73695e+06 [15] valid_0's l1: 1.71664e+06 [16] valid_0's l1: 1.70057e+06 [17] valid_0's l1: 1.68575e+06 [18] valid_0's l1: 1.68101e+06 [19] valid_0's l1: 1.66905e+06 [20] valid_0's l1: 1.66859e+06 [21] valid_0's l1: 1.67162e+06 [22] valid_0's l1: 1.6639e+06 [23] valid_0's l1: 1.66392e+06 [24] valid_0's l1: 1.65765e+06 [25] valid_0's l1: 1.65855e+06 [26] valid_0's l1: 1.66059e+06 [27] valid_0's l1: 1.65435e+06 [28] valid_0's l1: 1.65028e+06 [29] valid_0's l1: 1.64869e+06 [30] valid_0's l1: 1.64393e+06 [31] valid_0's l1: 1.64521e+06 [32] valid_0's l1: 1.6455e+06 [33] valid_0's l1: 1.64542e+06 [34] valid_0's l1: 1.64099e+06 [35] valid_0's l1: 1.64512e+06 [36] valid_0's l1: 1.64712e+06 [37] valid_0's l1: 1.64712e+06 [38] valid_0's l1: 1.64563e+06 [39] valid_0's l1: 1.64968e+06 [40] valid_0's l1: 1.65098e+06 [41] valid_0's l1: 1.64928e+06 [42] valid_0's l1: 1.64421e+06 [43] valid_0's l1: 1.64426e+06 [44] valid_0's l1: 1.64907e+06 [45] valid_0's l1: 1.6469e+06 [46] valid_0's l1: 1.64921e+06 [47] valid_0's l1: 1.65081e+06 [48] valid_0's l1: 1.65122e+06 [49] valid_0's l1: 1.65125e+06 [50] valid_0's l1: 1.65428e+06 [51] valid_0's l1: 1.65118e+06 [52] valid_0's l1: 1.6508e+06 [53] valid_0's l1: 1.65457e+06 [54] valid_0's l1: 1.65743e+06 [55] valid_0's l1: 1.65928e+06 [56] valid_0's l1: 1.65673e+06 [57] valid_0's l1: 1.65866e+06 [58] valid_0's l1: 1.66043e+06 [59] valid_0's l1: 1.65911e+06 [60] valid_0's l1: 1.65978e+06 [61] valid_0's l1: 1.66008e+06 [62] valid_0's l1: 1.65961e+06 [63] valid_0's l1: 1.65945e+06 [64] valid_0's l1: 1.66602e+06 Early stopping, best iteration is: [34] valid_0's l1: 1.64099e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.68661e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.1591e+06 [3] valid_0's l1: 2.75499e+06 [4] valid_0's l1: 2.45416e+06 [5] valid_0's l1: 2.23723e+06 [6] valid_0's l1: 2.1592e+06 [7] valid_0's l1: 2.10718e+06 [8] valid_0's l1: 1.98413e+06 [9] valid_0's l1: 1.89977e+06 [10] valid_0's l1: 1.83449e+06 [11] valid_0's l1: 1.78458e+06 [12] valid_0's l1: 1.76969e+06 [13] valid_0's l1: 1.73441e+06 [14] valid_0's l1: 1.73006e+06 [15] valid_0's l1: 1.7097e+06 [16] valid_0's l1: 1.69813e+06 [17] valid_0's l1: 1.69385e+06 [18] valid_0's l1: 1.67542e+06 [19] valid_0's l1: 1.66869e+06 [20] valid_0's l1: 1.66645e+06 [21] valid_0's l1: 1.66198e+06 [22] valid_0's l1: 1.663e+06 [23] valid_0's l1: 1.65616e+06 [24] valid_0's l1: 1.65922e+06 [25] valid_0's l1: 1.65335e+06 [26] valid_0's l1: 1.6563e+06 [27] valid_0's l1: 1.65191e+06 [28] valid_0's l1: 1.65176e+06 [29] valid_0's l1: 1.6513e+06 [30] valid_0's l1: 1.64843e+06 [31] valid_0's l1: 1.65164e+06 [32] valid_0's l1: 1.64712e+06 [33] valid_0's l1: 1.64766e+06 [34] valid_0's l1: 1.6514e+06 [35] valid_0's l1: 1.65158e+06 [36] valid_0's l1: 1.65444e+06 [37] valid_0's l1: 1.6581e+06 [38] valid_0's l1: 1.65599e+06 [39] valid_0's l1: 1.65938e+06 [40] valid_0's l1: 1.66228e+06 [41] valid_0's l1: 1.66307e+06 [42] valid_0's l1: 1.66281e+06 [43] valid_0's l1: 1.66354e+06 [44] valid_0's l1: 1.6595e+06 [45] valid_0's l1: 1.66067e+06 [46] valid_0's l1: 1.65911e+06 [47] valid_0's l1: 1.66115e+06 [48] valid_0's l1: 1.65837e+06 [49] valid_0's l1: 1.65968e+06 [50] valid_0's l1: 1.65673e+06 [51] valid_0's l1: 1.65973e+06 [52] valid_0's l1: 1.6606e+06 [53] valid_0's l1: 1.6596e+06 [54] valid_0's l1: 1.66112e+06 [55] valid_0's l1: 1.65963e+06 [56] valid_0's l1: 1.66089e+06 [57] valid_0's l1: 1.65919e+06 [58] valid_0's l1: 1.6567e+06 [59] valid_0's l1: 1.65635e+06 [60] valid_0's l1: 1.65687e+06 [61] valid_0's l1: 1.65557e+06 [62] valid_0's l1: 1.65768e+06 Early stopping, best iteration is: [32] valid_0's l1: 1.64712e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.75296e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.19259e+06 [3] valid_0's l1: 2.7636e+06 [4] valid_0's l1: 2.45106e+06 [5] valid_0's l1: 2.23614e+06 [6] valid_0's l1: 2.17392e+06 [7] valid_0's l1: 2.12559e+06 [8] valid_0's l1: 1.99899e+06 [9] valid_0's l1: 1.9085e+06 [10] valid_0's l1: 1.83877e+06 [11] valid_0's l1: 1.78734e+06 [12] valid_0's l1: 1.75899e+06 [13] valid_0's l1: 1.71648e+06 [14] valid_0's l1: 1.71539e+06 [15] valid_0's l1: 1.68911e+06 [16] valid_0's l1: 1.67195e+06 [17] valid_0's l1: 1.65462e+06 [18] valid_0's l1: 1.65416e+06 [19] valid_0's l1: 1.65014e+06 [20] valid_0's l1: 1.64412e+06 [21] valid_0's l1: 1.6371e+06 [22] valid_0's l1: 1.63928e+06 [23] valid_0's l1: 1.63282e+06 [24] valid_0's l1: 1.63717e+06 [25] valid_0's l1: 1.63757e+06 [26] valid_0's l1: 1.63509e+06 [27] valid_0's l1: 1.63896e+06 [28] valid_0's l1: 1.63966e+06 [29] valid_0's l1: 1.6405e+06 [30] valid_0's l1: 1.64241e+06 [31] valid_0's l1: 1.64361e+06 [32] valid_0's l1: 1.64613e+06 [33] valid_0's l1: 1.64798e+06 [34] valid_0's l1: 1.64872e+06 [35] valid_0's l1: 1.64828e+06 [36] valid_0's l1: 1.64506e+06 [37] valid_0's l1: 1.64929e+06 [38] valid_0's l1: 1.64905e+06 [39] valid_0's l1: 1.65143e+06 [40] valid_0's l1: 1.64509e+06 [41] valid_0's l1: 1.64443e+06 [42] valid_0's l1: 1.64504e+06 [43] valid_0's l1: 1.64629e+06 [44] valid_0's l1: 1.64587e+06 [45] valid_0's l1: 1.64659e+06 [46] valid_0's l1: 1.6472e+06 [47] valid_0's l1: 1.65008e+06 [48] valid_0's l1: 1.65641e+06 [49] valid_0's l1: 1.65651e+06 [50] valid_0's l1: 1.65755e+06 [51] valid_0's l1: 1.65789e+06 [52] valid_0's l1: 1.65927e+06 [53] valid_0's l1: 1.65914e+06 Early stopping, best iteration is: [23] valid_0's l1: 1.63282e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.7301e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.1745e+06 [3] valid_0's l1: 2.77688e+06 [4] valid_0's l1: 2.47445e+06 [5] valid_0's l1: 2.26323e+06 [6] valid_0's l1: 2.20126e+06 [7] valid_0's l1: 2.15119e+06 [8] valid_0's l1: 2.01153e+06 [9] valid_0's l1: 1.90874e+06 [10] valid_0's l1: 1.84571e+06 [11] valid_0's l1: 1.78861e+06 [12] valid_0's l1: 1.77501e+06 [13] valid_0's l1: 1.7415e+06 [14] valid_0's l1: 1.73695e+06 [15] valid_0's l1: 1.71664e+06 [16] valid_0's l1: 1.70057e+06 [17] valid_0's l1: 1.68575e+06 [18] valid_0's l1: 1.68101e+06 [19] valid_0's l1: 1.66905e+06 [20] valid_0's l1: 1.66859e+06 [21] valid_0's l1: 1.67162e+06 [22] valid_0's l1: 1.6639e+06 [23] valid_0's l1: 1.66392e+06 [24] valid_0's l1: 1.65765e+06 [25] valid_0's l1: 1.65855e+06 [26] valid_0's l1: 1.66059e+06 [27] valid_0's l1: 1.65435e+06 [28] valid_0's l1: 1.65028e+06 [29] valid_0's l1: 1.64869e+06 [30] valid_0's l1: 1.64393e+06 [31] valid_0's l1: 1.64521e+06 [32] valid_0's l1: 1.6455e+06 [33] valid_0's l1: 1.64542e+06 [34] valid_0's l1: 1.64099e+06 [35] valid_0's l1: 1.64512e+06 [36] valid_0's l1: 1.64712e+06 [37] valid_0's l1: 1.64712e+06 [38] valid_0's l1: 1.64563e+06 [39] valid_0's l1: 1.64968e+06 [40] valid_0's l1: 1.65098e+06 [41] valid_0's l1: 1.64928e+06 [42] valid_0's l1: 1.64421e+06 [43] valid_0's l1: 1.64426e+06 [44] valid_0's l1: 1.64907e+06 [45] valid_0's l1: 1.6469e+06 [46] valid_0's l1: 1.64921e+06 [47] valid_0's l1: 1.65081e+06 [48] valid_0's l1: 1.65122e+06 [49] valid_0's l1: 1.65125e+06 [50] valid_0's l1: 1.65428e+06 [51] valid_0's l1: 1.65118e+06 [52] valid_0's l1: 1.6508e+06 [53] valid_0's l1: 1.65457e+06 [54] valid_0's l1: 1.65743e+06 [55] valid_0's l1: 1.65928e+06 [56] valid_0's l1: 1.65673e+06 [57] valid_0's l1: 1.65866e+06 [58] valid_0's l1: 1.66043e+06 [59] valid_0's l1: 1.65911e+06 [60] valid_0's l1: 1.65978e+06 [61] valid_0's l1: 1.66008e+06 [62] valid_0's l1: 1.65961e+06 [63] valid_0's l1: 1.65945e+06 [64] valid_0's l1: 1.66602e+06 Early stopping, best iteration is: [34] valid_0's l1: 1.64099e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.68661e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.1591e+06 [3] valid_0's l1: 2.75499e+06 [4] valid_0's l1: 2.45416e+06 [5] valid_0's l1: 2.23723e+06 [6] valid_0's l1: 2.1592e+06 [7] valid_0's l1: 2.10718e+06 [8] valid_0's l1: 1.98413e+06 [9] valid_0's l1: 1.89977e+06 [10] valid_0's l1: 1.83449e+06 [11] valid_0's l1: 1.78458e+06 [12] valid_0's l1: 1.76969e+06 [13] valid_0's l1: 1.73441e+06 [14] valid_0's l1: 1.73006e+06 [15] valid_0's l1: 1.7097e+06 [16] valid_0's l1: 1.69813e+06 [17] valid_0's l1: 1.69385e+06 [18] valid_0's l1: 1.67542e+06 [19] valid_0's l1: 1.66869e+06 [20] valid_0's l1: 1.66645e+06 [21] valid_0's l1: 1.66198e+06 [22] valid_0's l1: 1.663e+06 [23] valid_0's l1: 1.65616e+06 [24] valid_0's l1: 1.65922e+06 [25] valid_0's l1: 1.65335e+06 [26] valid_0's l1: 1.6563e+06 [27] valid_0's l1: 1.65191e+06 [28] valid_0's l1: 1.65176e+06 [29] valid_0's l1: 1.6513e+06 [30] valid_0's l1: 1.64843e+06 [31] valid_0's l1: 1.65164e+06 [32] valid_0's l1: 1.64712e+06 [33] valid_0's l1: 1.64766e+06 [34] valid_0's l1: 1.6514e+06 [35] valid_0's l1: 1.65158e+06 [36] valid_0's l1: 1.65444e+06 [37] valid_0's l1: 1.6581e+06 [38] valid_0's l1: 1.65599e+06 [39] valid_0's l1: 1.65938e+06 [40] valid_0's l1: 1.66228e+06 [41] valid_0's l1: 1.66307e+06 [42] valid_0's l1: 1.66281e+06 [43] valid_0's l1: 1.66354e+06 [44] valid_0's l1: 1.6595e+06 [45] valid_0's l1: 1.66067e+06 [46] valid_0's l1: 1.65911e+06 [47] valid_0's l1: 1.66115e+06 [48] valid_0's l1: 1.65837e+06 [49] valid_0's l1: 1.65968e+06 [50] valid_0's l1: 1.65673e+06 [51] valid_0's l1: 1.65973e+06 [52] valid_0's l1: 1.6606e+06 [53] valid_0's l1: 1.6596e+06 [54] valid_0's l1: 1.66112e+06 [55] valid_0's l1: 1.65963e+06 [56] valid_0's l1: 1.66089e+06 [57] valid_0's l1: 1.65919e+06 [58] valid_0's l1: 1.6567e+06 [59] valid_0's l1: 1.65635e+06 [60] valid_0's l1: 1.65687e+06 [61] valid_0's l1: 1.65557e+06 [62] valid_0's l1: 1.65768e+06 Early stopping, best iteration is: [32] valid_0's l1: 1.64712e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.75296e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.19259e+06 [3] valid_0's l1: 2.7636e+06 [4] valid_0's l1: 2.45106e+06 [5] valid_0's l1: 2.23614e+06 [6] valid_0's l1: 2.17392e+06 [7] valid_0's l1: 2.12559e+06 [8] valid_0's l1: 1.99899e+06 [9] valid_0's l1: 1.9085e+06 [10] valid_0's l1: 1.83877e+06 [11] valid_0's l1: 1.78734e+06 [12] valid_0's l1: 1.75899e+06 [13] valid_0's l1: 1.71648e+06 [14] valid_0's l1: 1.71539e+06 [15] valid_0's l1: 1.68911e+06 [16] valid_0's l1: 1.67195e+06 [17] valid_0's l1: 1.65462e+06 [18] valid_0's l1: 1.65416e+06 [19] valid_0's l1: 1.65014e+06 [20] valid_0's l1: 1.64412e+06 [21] valid_0's l1: 1.6371e+06 [22] valid_0's l1: 1.63928e+06 [23] valid_0's l1: 1.63282e+06 [24] valid_0's l1: 1.63717e+06 [25] valid_0's l1: 1.63757e+06 [26] valid_0's l1: 1.63509e+06 [27] valid_0's l1: 1.63896e+06 [28] valid_0's l1: 1.63966e+06 [29] valid_0's l1: 1.6405e+06 [30] valid_0's l1: 1.64241e+06 [31] valid_0's l1: 1.64361e+06 [32] valid_0's l1: 1.64613e+06 [33] valid_0's l1: 1.64798e+06 [34] valid_0's l1: 1.64872e+06 [35] valid_0's l1: 1.64828e+06 [36] valid_0's l1: 1.64506e+06 [37] valid_0's l1: 1.64929e+06 [38] valid_0's l1: 1.64905e+06 [39] valid_0's l1: 1.65143e+06 [40] valid_0's l1: 1.64509e+06 [41] valid_0's l1: 1.64443e+06 [42] valid_0's l1: 1.64504e+06 [43] valid_0's l1: 1.64629e+06 [44] valid_0's l1: 1.64587e+06 [45] valid_0's l1: 1.64659e+06 [46] valid_0's l1: 1.6472e+06 [47] valid_0's l1: 1.65008e+06 [48] valid_0's l1: 1.65641e+06 [49] valid_0's l1: 1.65651e+06 [50] valid_0's l1: 1.65755e+06 [51] valid_0's l1: 1.65789e+06 [52] valid_0's l1: 1.65927e+06 [53] valid_0's l1: 1.65914e+06 Early stopping, best iteration is: [23] valid_0's l1: 1.63282e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.7301e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.1745e+06 [3] valid_0's l1: 2.77688e+06 [4] valid_0's l1: 2.47445e+06 [5] valid_0's l1: 2.26323e+06 [6] valid_0's l1: 2.20126e+06 [7] valid_0's l1: 2.15119e+06 [8] valid_0's l1: 2.01153e+06 [9] valid_0's l1: 1.90874e+06 [10] valid_0's l1: 1.84571e+06 [11] valid_0's l1: 1.78861e+06 [12] valid_0's l1: 1.77501e+06 [13] valid_0's l1: 1.7415e+06 [14] valid_0's l1: 1.73695e+06 [15] valid_0's l1: 1.71664e+06 [16] valid_0's l1: 1.70057e+06 [17] valid_0's l1: 1.68575e+06 [18] valid_0's l1: 1.68101e+06 [19] valid_0's l1: 1.66905e+06 [20] valid_0's l1: 1.66859e+06 [21] valid_0's l1: 1.67162e+06 [22] valid_0's l1: 1.6639e+06 [23] valid_0's l1: 1.66392e+06 [24] valid_0's l1: 1.65765e+06 [25] valid_0's l1: 1.65855e+06 [26] valid_0's l1: 1.66059e+06 [27] valid_0's l1: 1.65435e+06 [28] valid_0's l1: 1.65028e+06 [29] valid_0's l1: 1.64869e+06 [30] valid_0's l1: 1.64393e+06 [31] valid_0's l1: 1.64521e+06 [32] valid_0's l1: 1.6455e+06 [33] valid_0's l1: 1.64542e+06 [34] valid_0's l1: 1.64099e+06 [35] valid_0's l1: 1.64512e+06 [36] valid_0's l1: 1.64712e+06 [37] valid_0's l1: 1.64712e+06 [38] valid_0's l1: 1.64563e+06 [39] valid_0's l1: 1.64968e+06 [40] valid_0's l1: 1.65098e+06 [41] valid_0's l1: 1.64928e+06 [42] valid_0's l1: 1.64421e+06 [43] valid_0's l1: 1.64426e+06 [44] valid_0's l1: 1.64907e+06 [45] valid_0's l1: 1.6469e+06 [46] valid_0's l1: 1.64921e+06 [47] valid_0's l1: 1.65081e+06 [48] valid_0's l1: 1.65122e+06 [49] valid_0's l1: 1.65125e+06 [50] valid_0's l1: 1.65428e+06 [51] valid_0's l1: 1.65118e+06 [52] valid_0's l1: 1.6508e+06 [53] valid_0's l1: 1.65457e+06 [54] valid_0's l1: 1.65743e+06 [55] valid_0's l1: 1.65928e+06 [56] valid_0's l1: 1.65673e+06 [57] valid_0's l1: 1.65866e+06 [58] valid_0's l1: 1.66043e+06 [59] valid_0's l1: 1.65911e+06 [60] valid_0's l1: 1.65978e+06 [61] valid_0's l1: 1.66008e+06 [62] valid_0's l1: 1.65961e+06 [63] valid_0's l1: 1.65945e+06 [64] valid_0's l1: 1.66602e+06 Early stopping, best iteration is: [34] valid_0's l1: 1.64099e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.68661e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.1591e+06 [3] valid_0's l1: 2.75499e+06 [4] valid_0's l1: 2.45416e+06 [5] valid_0's l1: 2.23723e+06 [6] valid_0's l1: 2.1592e+06 [7] valid_0's l1: 2.10718e+06 [8] valid_0's l1: 1.98413e+06 [9] valid_0's l1: 1.89977e+06 [10] valid_0's l1: 1.83449e+06 [11] valid_0's l1: 1.78458e+06 [12] valid_0's l1: 1.76969e+06 [13] valid_0's l1: 1.73441e+06 [14] valid_0's l1: 1.73006e+06 [15] valid_0's l1: 1.7097e+06 [16] valid_0's l1: 1.69813e+06 [17] valid_0's l1: 1.69385e+06 [18] valid_0's l1: 1.67542e+06 [19] valid_0's l1: 1.66869e+06 [20] valid_0's l1: 1.66645e+06 [21] valid_0's l1: 1.66198e+06 [22] valid_0's l1: 1.663e+06 [23] valid_0's l1: 1.65616e+06 [24] valid_0's l1: 1.65922e+06 [25] valid_0's l1: 1.65335e+06 [26] valid_0's l1: 1.6563e+06 [27] valid_0's l1: 1.65191e+06 [28] valid_0's l1: 1.65176e+06 [29] valid_0's l1: 1.6513e+06 [30] valid_0's l1: 1.64843e+06 [31] valid_0's l1: 1.65164e+06 [32] valid_0's l1: 1.64712e+06 [33] valid_0's l1: 1.64766e+06 [34] valid_0's l1: 1.6514e+06 [35] valid_0's l1: 1.65158e+06 [36] valid_0's l1: 1.65444e+06 [37] valid_0's l1: 1.6581e+06 [38] valid_0's l1: 1.65599e+06 [39] valid_0's l1: 1.65938e+06 [40] valid_0's l1: 1.66228e+06 [41] valid_0's l1: 1.66307e+06 [42] valid_0's l1: 1.66281e+06 [43] valid_0's l1: 1.66354e+06 [44] valid_0's l1: 1.6595e+06 [45] valid_0's l1: 1.66067e+06 [46] valid_0's l1: 1.65911e+06 [47] valid_0's l1: 1.66115e+06 [48] valid_0's l1: 1.65837e+06 [49] valid_0's l1: 1.65968e+06 [50] valid_0's l1: 1.65673e+06 [51] valid_0's l1: 1.65973e+06 [52] valid_0's l1: 1.6606e+06 [53] valid_0's l1: 1.6596e+06 [54] valid_0's l1: 1.66112e+06 [55] valid_0's l1: 1.65963e+06 [56] valid_0's l1: 1.66089e+06 [57] valid_0's l1: 1.65919e+06 [58] valid_0's l1: 1.6567e+06 [59] valid_0's l1: 1.65635e+06 [60] valid_0's l1: 1.65687e+06 [61] valid_0's l1: 1.65557e+06 [62] valid_0's l1: 1.65768e+06 Early stopping, best iteration is: [32] valid_0's l1: 1.64712e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.75296e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.19259e+06 [3] valid_0's l1: 2.7636e+06 [4] valid_0's l1: 2.45106e+06 [5] valid_0's l1: 2.23614e+06 [6] valid_0's l1: 2.17392e+06 [7] valid_0's l1: 2.12559e+06 [8] valid_0's l1: 1.99899e+06 [9] valid_0's l1: 1.9085e+06 [10] valid_0's l1: 1.83877e+06 [11] valid_0's l1: 1.78734e+06 [12] valid_0's l1: 1.75899e+06 [13] valid_0's l1: 1.71648e+06 [14] valid_0's l1: 1.71539e+06 [15] valid_0's l1: 1.68911e+06 [16] valid_0's l1: 1.67195e+06 [17] valid_0's l1: 1.65462e+06 [18] valid_0's l1: 1.65416e+06 [19] valid_0's l1: 1.65014e+06 [20] valid_0's l1: 1.64412e+06 [21] valid_0's l1: 1.6371e+06 [22] valid_0's l1: 1.63928e+06 [23] valid_0's l1: 1.63282e+06 [24] valid_0's l1: 1.63717e+06 [25] valid_0's l1: 1.63757e+06 [26] valid_0's l1: 1.63509e+06 [27] valid_0's l1: 1.63896e+06 [28] valid_0's l1: 1.63966e+06 [29] valid_0's l1: 1.6405e+06 [30] valid_0's l1: 1.64241e+06 [31] valid_0's l1: 1.64361e+06 [32] valid_0's l1: 1.64613e+06 [33] valid_0's l1: 1.64798e+06 [34] valid_0's l1: 1.64872e+06 [35] valid_0's l1: 1.64828e+06 [36] valid_0's l1: 1.64506e+06 [37] valid_0's l1: 1.64929e+06 [38] valid_0's l1: 1.64905e+06 [39] valid_0's l1: 1.65143e+06 [40] valid_0's l1: 1.64509e+06 [41] valid_0's l1: 1.64443e+06 [42] valid_0's l1: 1.64504e+06 [43] valid_0's l1: 1.64629e+06 [44] valid_0's l1: 1.64587e+06 [45] valid_0's l1: 1.64659e+06 [46] valid_0's l1: 1.6472e+06 [47] valid_0's l1: 1.65008e+06 [48] valid_0's l1: 1.65641e+06 [49] valid_0's l1: 1.65651e+06 [50] valid_0's l1: 1.65755e+06 [51] valid_0's l1: 1.65789e+06 [52] valid_0's l1: 1.65927e+06 [53] valid_0's l1: 1.65914e+06 Early stopping, best iteration is: [23] valid_0's l1: 1.63282e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.7172e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.17345e+06 [3] valid_0's l1: 2.77403e+06 [4] valid_0's l1: 2.47886e+06 [5] valid_0's l1: 2.26288e+06 [6] valid_0's l1: 2.19668e+06 [7] valid_0's l1: 2.14756e+06 [8] valid_0's l1: 2.00628e+06 [9] valid_0's l1: 1.90309e+06 [10] valid_0's l1: 1.84191e+06 [11] valid_0's l1: 1.78333e+06 [12] valid_0's l1: 1.75664e+06 [13] valid_0's l1: 1.72714e+06 [14] valid_0's l1: 1.72713e+06 [15] valid_0's l1: 1.70508e+06 [16] valid_0's l1: 1.68751e+06 [17] valid_0's l1: 1.67159e+06 [18] valid_0's l1: 1.66508e+06 [19] valid_0's l1: 1.66682e+06 [20] valid_0's l1: 1.65486e+06 [21] valid_0's l1: 1.65807e+06 [22] valid_0's l1: 1.65513e+06 [23] valid_0's l1: 1.65293e+06 [24] valid_0's l1: 1.65676e+06 [25] valid_0's l1: 1.6567e+06 [26] valid_0's l1: 1.65246e+06 [27] valid_0's l1: 1.65489e+06 [28] valid_0's l1: 1.6542e+06 [29] valid_0's l1: 1.65756e+06 [30] valid_0's l1: 1.64798e+06 [31] valid_0's l1: 1.64311e+06 [32] valid_0's l1: 1.64597e+06 [33] valid_0's l1: 1.6487e+06 [34] valid_0's l1: 1.65293e+06 [35] valid_0's l1: 1.65345e+06 [36] valid_0's l1: 1.65514e+06 [37] valid_0's l1: 1.65351e+06 [38] valid_0's l1: 1.65272e+06 [39] valid_0's l1: 1.65673e+06 [40] valid_0's l1: 1.65663e+06 [41] valid_0's l1: 1.65784e+06 [42] valid_0's l1: 1.65672e+06 [43] valid_0's l1: 1.65752e+06 [44] valid_0's l1: 1.65753e+06 [45] valid_0's l1: 1.65528e+06 [46] valid_0's l1: 1.66035e+06 [47] valid_0's l1: 1.66167e+06 [48] valid_0's l1: 1.66245e+06 [49] valid_0's l1: 1.66084e+06 [50] valid_0's l1: 1.66428e+06 [51] valid_0's l1: 1.66446e+06 [52] valid_0's l1: 1.66727e+06 [53] valid_0's l1: 1.67062e+06 [54] valid_0's l1: 1.67354e+06 [55] valid_0's l1: 1.67543e+06 [56] valid_0's l1: 1.67659e+06 [57] valid_0's l1: 1.67716e+06 [58] valid_0's l1: 1.67859e+06 [59] valid_0's l1: 1.68064e+06 [60] valid_0's l1: 1.68223e+06 [61] valid_0's l1: 1.68239e+06 Early stopping, best iteration is: [31] valid_0's l1: 1.64311e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.67303e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.15957e+06 [3] valid_0's l1: 2.74986e+06 [4] valid_0's l1: 2.45151e+06 [5] valid_0's l1: 2.23013e+06 [6] valid_0's l1: 2.15413e+06 [7] valid_0's l1: 2.08852e+06 [8] valid_0's l1: 1.97426e+06 [9] valid_0's l1: 1.88784e+06 [10] valid_0's l1: 1.82639e+06 [11] valid_0's l1: 1.77641e+06 [12] valid_0's l1: 1.7611e+06 [13] valid_0's l1: 1.7281e+06 [14] valid_0's l1: 1.71859e+06 [15] valid_0's l1: 1.69797e+06 [16] valid_0's l1: 1.68329e+06 [17] valid_0's l1: 1.68038e+06 [18] valid_0's l1: 1.66907e+06 [19] valid_0's l1: 1.66621e+06 [20] valid_0's l1: 1.66287e+06 [21] valid_0's l1: 1.66018e+06 [22] valid_0's l1: 1.66398e+06 [23] valid_0's l1: 1.66027e+06 [24] valid_0's l1: 1.66435e+06 [25] valid_0's l1: 1.65992e+06 [26] valid_0's l1: 1.66286e+06 [27] valid_0's l1: 1.65973e+06 [28] valid_0's l1: 1.65974e+06 [29] valid_0's l1: 1.6571e+06 [30] valid_0's l1: 1.65502e+06 [31] valid_0's l1: 1.65876e+06 [32] valid_0's l1: 1.66014e+06 [33] valid_0's l1: 1.6561e+06 [34] valid_0's l1: 1.65872e+06 [35] valid_0's l1: 1.65888e+06 [36] valid_0's l1: 1.65659e+06 [37] valid_0's l1: 1.65984e+06 [38] valid_0's l1: 1.65935e+06 [39] valid_0's l1: 1.6611e+06 [40] valid_0's l1: 1.66236e+06 [41] valid_0's l1: 1.65998e+06 [42] valid_0's l1: 1.66392e+06 [43] valid_0's l1: 1.66179e+06 [44] valid_0's l1: 1.66272e+06 [45] valid_0's l1: 1.66274e+06 [46] valid_0's l1: 1.66192e+06 [47] valid_0's l1: 1.66486e+06 [48] valid_0's l1: 1.66293e+06 [49] valid_0's l1: 1.6636e+06 [50] valid_0's l1: 1.66386e+06 [51] valid_0's l1: 1.66824e+06 [52] valid_0's l1: 1.66677e+06 [53] valid_0's l1: 1.67165e+06 [54] valid_0's l1: 1.67538e+06 [55] valid_0's l1: 1.67742e+06 [56] valid_0's l1: 1.67659e+06 [57] valid_0's l1: 1.67701e+06 [58] valid_0's l1: 1.67817e+06 [59] valid_0's l1: 1.67647e+06 [60] valid_0's l1: 1.67678e+06 Early stopping, best iteration is: [30] valid_0's l1: 1.65502e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.74198e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.17925e+06 [3] valid_0's l1: 2.75493e+06 [4] valid_0's l1: 2.43331e+06 [5] valid_0's l1: 2.21332e+06 [6] valid_0's l1: 2.1413e+06 [7] valid_0's l1: 2.09055e+06 [8] valid_0's l1: 1.97663e+06 [9] valid_0's l1: 1.88299e+06 [10] valid_0's l1: 1.81844e+06 [11] valid_0's l1: 1.76375e+06 [12] valid_0's l1: 1.74997e+06 [13] valid_0's l1: 1.71063e+06 [14] valid_0's l1: 1.70095e+06 [15] valid_0's l1: 1.68101e+06 [16] valid_0's l1: 1.6596e+06 [17] valid_0's l1: 1.65919e+06 [18] valid_0's l1: 1.64736e+06 [19] valid_0's l1: 1.64521e+06 [20] valid_0's l1: 1.64499e+06 [21] valid_0's l1: 1.63518e+06 [22] valid_0's l1: 1.63929e+06 [23] valid_0's l1: 1.64545e+06 [24] valid_0's l1: 1.64718e+06 [25] valid_0's l1: 1.64141e+06 [26] valid_0's l1: 1.64456e+06 [27] valid_0's l1: 1.64418e+06 [28] valid_0's l1: 1.64485e+06 [29] valid_0's l1: 1.65057e+06 [30] valid_0's l1: 1.65105e+06 [31] valid_0's l1: 1.65194e+06 [32] valid_0's l1: 1.65609e+06 [33] valid_0's l1: 1.65301e+06 [34] valid_0's l1: 1.65495e+06 [35] valid_0's l1: 1.65622e+06 [36] valid_0's l1: 1.66002e+06 [37] valid_0's l1: 1.66264e+06 [38] valid_0's l1: 1.66355e+06 [39] valid_0's l1: 1.66487e+06 [40] valid_0's l1: 1.66699e+06 [41] valid_0's l1: 1.66554e+06 [42] valid_0's l1: 1.66476e+06 [43] valid_0's l1: 1.66452e+06 [44] valid_0's l1: 1.66608e+06 [45] valid_0's l1: 1.66929e+06 [46] valid_0's l1: 1.66908e+06 [47] valid_0's l1: 1.66932e+06 [48] valid_0's l1: 1.66901e+06 [49] valid_0's l1: 1.66908e+06 [50] valid_0's l1: 1.67006e+06 [51] valid_0's l1: 1.66873e+06 Early stopping, best iteration is: [21] valid_0's l1: 1.63518e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.7172e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.17345e+06 [3] valid_0's l1: 2.77403e+06 [4] valid_0's l1: 2.47886e+06 [5] valid_0's l1: 2.26288e+06 [6] valid_0's l1: 2.19668e+06 [7] valid_0's l1: 2.14756e+06 [8] valid_0's l1: 2.00628e+06 [9] valid_0's l1: 1.90309e+06 [10] valid_0's l1: 1.84191e+06 [11] valid_0's l1: 1.78333e+06 [12] valid_0's l1: 1.75664e+06 [13] valid_0's l1: 1.72714e+06 [14] valid_0's l1: 1.72713e+06 [15] valid_0's l1: 1.70508e+06 [16] valid_0's l1: 1.68751e+06 [17] valid_0's l1: 1.67159e+06 [18] valid_0's l1: 1.66508e+06 [19] valid_0's l1: 1.66682e+06 [20] valid_0's l1: 1.65486e+06 [21] valid_0's l1: 1.65807e+06 [22] valid_0's l1: 1.65513e+06 [23] valid_0's l1: 1.65293e+06 [24] valid_0's l1: 1.65676e+06 [25] valid_0's l1: 1.6567e+06 [26] valid_0's l1: 1.65246e+06 [27] valid_0's l1: 1.65489e+06 [28] valid_0's l1: 1.6542e+06 [29] valid_0's l1: 1.65756e+06 [30] valid_0's l1: 1.64798e+06 [31] valid_0's l1: 1.64311e+06 [32] valid_0's l1: 1.64597e+06 [33] valid_0's l1: 1.6487e+06 [34] valid_0's l1: 1.65293e+06 [35] valid_0's l1: 1.65345e+06 [36] valid_0's l1: 1.65514e+06 [37] valid_0's l1: 1.65351e+06 [38] valid_0's l1: 1.65272e+06 [39] valid_0's l1: 1.65673e+06 [40] valid_0's l1: 1.65663e+06 [41] valid_0's l1: 1.65784e+06 [42] valid_0's l1: 1.65672e+06 [43] valid_0's l1: 1.65752e+06 [44] valid_0's l1: 1.65753e+06 [45] valid_0's l1: 1.65528e+06 [46] valid_0's l1: 1.66035e+06 [47] valid_0's l1: 1.66167e+06 [48] valid_0's l1: 1.66245e+06 [49] valid_0's l1: 1.66084e+06 [50] valid_0's l1: 1.66428e+06 [51] valid_0's l1: 1.66446e+06 [52] valid_0's l1: 1.66727e+06 [53] valid_0's l1: 1.67062e+06 [54] valid_0's l1: 1.67354e+06 [55] valid_0's l1: 1.67543e+06 [56] valid_0's l1: 1.67659e+06 [57] valid_0's l1: 1.67716e+06 [58] valid_0's l1: 1.67859e+06 [59] valid_0's l1: 1.68064e+06 [60] valid_0's l1: 1.68223e+06 [61] valid_0's l1: 1.68239e+06 Early stopping, best iteration is: [31] valid_0's l1: 1.64311e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.67303e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.15957e+06 [3] valid_0's l1: 2.74986e+06 [4] valid_0's l1: 2.45151e+06 [5] valid_0's l1: 2.23013e+06 [6] valid_0's l1: 2.15413e+06 [7] valid_0's l1: 2.08852e+06 [8] valid_0's l1: 1.97426e+06 [9] valid_0's l1: 1.88784e+06 [10] valid_0's l1: 1.82639e+06 [11] valid_0's l1: 1.77641e+06 [12] valid_0's l1: 1.7611e+06 [13] valid_0's l1: 1.7281e+06 [14] valid_0's l1: 1.71859e+06 [15] valid_0's l1: 1.69797e+06 [16] valid_0's l1: 1.68329e+06 [17] valid_0's l1: 1.68038e+06 [18] valid_0's l1: 1.66907e+06 [19] valid_0's l1: 1.66621e+06 [20] valid_0's l1: 1.66287e+06 [21] valid_0's l1: 1.66018e+06 [22] valid_0's l1: 1.66398e+06 [23] valid_0's l1: 1.66027e+06 [24] valid_0's l1: 1.66435e+06 [25] valid_0's l1: 1.65992e+06 [26] valid_0's l1: 1.66286e+06 [27] valid_0's l1: 1.65973e+06 [28] valid_0's l1: 1.65974e+06 [29] valid_0's l1: 1.6571e+06 [30] valid_0's l1: 1.65502e+06 [31] valid_0's l1: 1.65876e+06 [32] valid_0's l1: 1.66014e+06 [33] valid_0's l1: 1.6561e+06 [34] valid_0's l1: 1.65872e+06 [35] valid_0's l1: 1.65888e+06 [36] valid_0's l1: 1.65659e+06 [37] valid_0's l1: 1.65984e+06 [38] valid_0's l1: 1.65935e+06 [39] valid_0's l1: 1.6611e+06 [40] valid_0's l1: 1.66236e+06 [41] valid_0's l1: 1.65998e+06 [42] valid_0's l1: 1.66392e+06 [43] valid_0's l1: 1.66179e+06 [44] valid_0's l1: 1.66272e+06 [45] valid_0's l1: 1.66274e+06 [46] valid_0's l1: 1.66192e+06 [47] valid_0's l1: 1.66486e+06 [48] valid_0's l1: 1.66293e+06 [49] valid_0's l1: 1.6636e+06 [50] valid_0's l1: 1.66386e+06 [51] valid_0's l1: 1.66824e+06 [52] valid_0's l1: 1.66677e+06 [53] valid_0's l1: 1.67165e+06 [54] valid_0's l1: 1.67538e+06 [55] valid_0's l1: 1.67742e+06 [56] valid_0's l1: 1.67659e+06 [57] valid_0's l1: 1.67701e+06 [58] valid_0's l1: 1.67817e+06 [59] valid_0's l1: 1.67647e+06 [60] valid_0's l1: 1.67678e+06 Early stopping, best iteration is: [30] valid_0's l1: 1.65502e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.74198e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.17925e+06 [3] valid_0's l1: 2.75493e+06 [4] valid_0's l1: 2.43331e+06 [5] valid_0's l1: 2.21332e+06 [6] valid_0's l1: 2.1413e+06 [7] valid_0's l1: 2.09055e+06 [8] valid_0's l1: 1.97663e+06 [9] valid_0's l1: 1.88299e+06 [10] valid_0's l1: 1.81844e+06 [11] valid_0's l1: 1.76375e+06 [12] valid_0's l1: 1.74997e+06 [13] valid_0's l1: 1.71063e+06 [14] valid_0's l1: 1.70095e+06 [15] valid_0's l1: 1.68101e+06 [16] valid_0's l1: 1.6596e+06 [17] valid_0's l1: 1.65919e+06 [18] valid_0's l1: 1.64736e+06 [19] valid_0's l1: 1.64521e+06 [20] valid_0's l1: 1.64499e+06 [21] valid_0's l1: 1.63518e+06 [22] valid_0's l1: 1.63929e+06 [23] valid_0's l1: 1.64545e+06 [24] valid_0's l1: 1.64718e+06 [25] valid_0's l1: 1.64141e+06 [26] valid_0's l1: 1.64456e+06 [27] valid_0's l1: 1.64418e+06 [28] valid_0's l1: 1.64485e+06 [29] valid_0's l1: 1.65057e+06 [30] valid_0's l1: 1.65105e+06 [31] valid_0's l1: 1.65194e+06 [32] valid_0's l1: 1.65609e+06 [33] valid_0's l1: 1.65301e+06 [34] valid_0's l1: 1.65495e+06 [35] valid_0's l1: 1.65622e+06 [36] valid_0's l1: 1.66002e+06 [37] valid_0's l1: 1.66264e+06 [38] valid_0's l1: 1.66355e+06 [39] valid_0's l1: 1.66487e+06 [40] valid_0's l1: 1.66699e+06 [41] valid_0's l1: 1.66554e+06 [42] valid_0's l1: 1.66476e+06 [43] valid_0's l1: 1.66452e+06 [44] valid_0's l1: 1.66608e+06 [45] valid_0's l1: 1.66929e+06 [46] valid_0's l1: 1.66908e+06 [47] valid_0's l1: 1.66932e+06 [48] valid_0's l1: 1.66901e+06 [49] valid_0's l1: 1.66908e+06 [50] valid_0's l1: 1.67006e+06 [51] valid_0's l1: 1.66873e+06 Early stopping, best iteration is: [21] valid_0's l1: 1.63518e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.7172e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.17345e+06 [3] valid_0's l1: 2.77403e+06 [4] valid_0's l1: 2.47886e+06 [5] valid_0's l1: 2.26288e+06 [6] valid_0's l1: 2.19668e+06 [7] valid_0's l1: 2.14756e+06 [8] valid_0's l1: 2.00628e+06 [9] valid_0's l1: 1.90309e+06 [10] valid_0's l1: 1.84191e+06 [11] valid_0's l1: 1.78333e+06 [12] valid_0's l1: 1.75664e+06 [13] valid_0's l1: 1.72714e+06 [14] valid_0's l1: 1.72713e+06 [15] valid_0's l1: 1.70508e+06 [16] valid_0's l1: 1.68751e+06 [17] valid_0's l1: 1.67159e+06 [18] valid_0's l1: 1.66508e+06 [19] valid_0's l1: 1.66682e+06 [20] valid_0's l1: 1.65486e+06 [21] valid_0's l1: 1.65807e+06 [22] valid_0's l1: 1.65513e+06 [23] valid_0's l1: 1.65293e+06 [24] valid_0's l1: 1.65676e+06 [25] valid_0's l1: 1.6567e+06 [26] valid_0's l1: 1.65246e+06 [27] valid_0's l1: 1.65489e+06 [28] valid_0's l1: 1.6542e+06 [29] valid_0's l1: 1.65756e+06 [30] valid_0's l1: 1.64798e+06 [31] valid_0's l1: 1.64311e+06 [32] valid_0's l1: 1.64597e+06 [33] valid_0's l1: 1.6487e+06 [34] valid_0's l1: 1.65293e+06 [35] valid_0's l1: 1.65345e+06 [36] valid_0's l1: 1.65514e+06 [37] valid_0's l1: 1.65351e+06 [38] valid_0's l1: 1.65272e+06 [39] valid_0's l1: 1.65673e+06 [40] valid_0's l1: 1.65663e+06 [41] valid_0's l1: 1.65784e+06 [42] valid_0's l1: 1.65672e+06 [43] valid_0's l1: 1.65752e+06 [44] valid_0's l1: 1.65753e+06 [45] valid_0's l1: 1.65528e+06 [46] valid_0's l1: 1.66035e+06 [47] valid_0's l1: 1.66167e+06 [48] valid_0's l1: 1.66245e+06 [49] valid_0's l1: 1.66084e+06 [50] valid_0's l1: 1.66428e+06 [51] valid_0's l1: 1.66446e+06 [52] valid_0's l1: 1.66727e+06 [53] valid_0's l1: 1.67062e+06 [54] valid_0's l1: 1.67354e+06 [55] valid_0's l1: 1.67543e+06 [56] valid_0's l1: 1.67659e+06 [57] valid_0's l1: 1.67716e+06 [58] valid_0's l1: 1.67859e+06 [59] valid_0's l1: 1.68064e+06 [60] valid_0's l1: 1.68223e+06 [61] valid_0's l1: 1.68239e+06 Early stopping, best iteration is: [31] valid_0's l1: 1.64311e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.67303e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.15957e+06 [3] valid_0's l1: 2.74986e+06 [4] valid_0's l1: 2.45151e+06 [5] valid_0's l1: 2.23013e+06 [6] valid_0's l1: 2.15413e+06 [7] valid_0's l1: 2.08852e+06 [8] valid_0's l1: 1.97426e+06 [9] valid_0's l1: 1.88784e+06 [10] valid_0's l1: 1.82639e+06 [11] valid_0's l1: 1.77641e+06 [12] valid_0's l1: 1.7611e+06 [13] valid_0's l1: 1.7281e+06 [14] valid_0's l1: 1.71859e+06 [15] valid_0's l1: 1.69797e+06 [16] valid_0's l1: 1.68329e+06 [17] valid_0's l1: 1.68038e+06 [18] valid_0's l1: 1.66907e+06 [19] valid_0's l1: 1.66621e+06 [20] valid_0's l1: 1.66287e+06 [21] valid_0's l1: 1.66018e+06 [22] valid_0's l1: 1.66398e+06 [23] valid_0's l1: 1.66027e+06 [24] valid_0's l1: 1.66435e+06 [25] valid_0's l1: 1.65992e+06 [26] valid_0's l1: 1.66286e+06 [27] valid_0's l1: 1.65973e+06 [28] valid_0's l1: 1.65974e+06 [29] valid_0's l1: 1.6571e+06 [30] valid_0's l1: 1.65502e+06 [31] valid_0's l1: 1.65876e+06 [32] valid_0's l1: 1.66014e+06 [33] valid_0's l1: 1.6561e+06 [34] valid_0's l1: 1.65872e+06 [35] valid_0's l1: 1.65888e+06 [36] valid_0's l1: 1.65659e+06 [37] valid_0's l1: 1.65984e+06 [38] valid_0's l1: 1.65935e+06 [39] valid_0's l1: 1.6611e+06 [40] valid_0's l1: 1.66236e+06 [41] valid_0's l1: 1.65998e+06 [42] valid_0's l1: 1.66392e+06 [43] valid_0's l1: 1.66179e+06 [44] valid_0's l1: 1.66272e+06 [45] valid_0's l1: 1.66274e+06 [46] valid_0's l1: 1.66192e+06 [47] valid_0's l1: 1.66486e+06 [48] valid_0's l1: 1.66293e+06 [49] valid_0's l1: 1.6636e+06 [50] valid_0's l1: 1.66386e+06 [51] valid_0's l1: 1.66824e+06 [52] valid_0's l1: 1.66677e+06 [53] valid_0's l1: 1.67165e+06 [54] valid_0's l1: 1.67538e+06 [55] valid_0's l1: 1.67742e+06 [56] valid_0's l1: 1.67659e+06 [57] valid_0's l1: 1.67701e+06 [58] valid_0's l1: 1.67817e+06 [59] valid_0's l1: 1.67647e+06 [60] valid_0's l1: 1.67678e+06 Early stopping, best iteration is: [30] valid_0's l1: 1.65502e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.74198e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.17925e+06 [3] valid_0's l1: 2.75493e+06 [4] valid_0's l1: 2.43331e+06 [5] valid_0's l1: 2.21332e+06 [6] valid_0's l1: 2.1413e+06 [7] valid_0's l1: 2.09055e+06 [8] valid_0's l1: 1.97663e+06 [9] valid_0's l1: 1.88299e+06 [10] valid_0's l1: 1.81844e+06 [11] valid_0's l1: 1.76375e+06 [12] valid_0's l1: 1.74997e+06 [13] valid_0's l1: 1.71063e+06 [14] valid_0's l1: 1.70095e+06 [15] valid_0's l1: 1.68101e+06 [16] valid_0's l1: 1.6596e+06 [17] valid_0's l1: 1.65919e+06 [18] valid_0's l1: 1.64736e+06 [19] valid_0's l1: 1.64521e+06 [20] valid_0's l1: 1.64499e+06 [21] valid_0's l1: 1.63518e+06 [22] valid_0's l1: 1.63929e+06 [23] valid_0's l1: 1.64545e+06 [24] valid_0's l1: 1.64718e+06 [25] valid_0's l1: 1.64141e+06 [26] valid_0's l1: 1.64456e+06 [27] valid_0's l1: 1.64418e+06 [28] valid_0's l1: 1.64485e+06 [29] valid_0's l1: 1.65057e+06 [30] valid_0's l1: 1.65105e+06 [31] valid_0's l1: 1.65194e+06 [32] valid_0's l1: 1.65609e+06 [33] valid_0's l1: 1.65301e+06 [34] valid_0's l1: 1.65495e+06 [35] valid_0's l1: 1.65622e+06 [36] valid_0's l1: 1.66002e+06 [37] valid_0's l1: 1.66264e+06 [38] valid_0's l1: 1.66355e+06 [39] valid_0's l1: 1.66487e+06 [40] valid_0's l1: 1.66699e+06 [41] valid_0's l1: 1.66554e+06 [42] valid_0's l1: 1.66476e+06 [43] valid_0's l1: 1.66452e+06 [44] valid_0's l1: 1.66608e+06 [45] valid_0's l1: 1.66929e+06 [46] valid_0's l1: 1.66908e+06 [47] valid_0's l1: 1.66932e+06 [48] valid_0's l1: 1.66901e+06 [49] valid_0's l1: 1.66908e+06 [50] valid_0's l1: 1.67006e+06 [51] valid_0's l1: 1.66873e+06 Early stopping, best iteration is: [21] valid_0's l1: 1.63518e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.7172e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.17345e+06 [3] valid_0's l1: 2.77403e+06 [4] valid_0's l1: 2.47886e+06 [5] valid_0's l1: 2.26288e+06 [6] valid_0's l1: 2.19668e+06 [7] valid_0's l1: 2.14756e+06 [8] valid_0's l1: 2.00628e+06 [9] valid_0's l1: 1.90309e+06 [10] valid_0's l1: 1.84191e+06 [11] valid_0's l1: 1.78333e+06 [12] valid_0's l1: 1.75664e+06 [13] valid_0's l1: 1.72714e+06 [14] valid_0's l1: 1.72713e+06 [15] valid_0's l1: 1.70508e+06 [16] valid_0's l1: 1.68751e+06 [17] valid_0's l1: 1.67159e+06 [18] valid_0's l1: 1.66508e+06 [19] valid_0's l1: 1.66682e+06 [20] valid_0's l1: 1.65486e+06 [21] valid_0's l1: 1.65807e+06 [22] valid_0's l1: 1.65513e+06 [23] valid_0's l1: 1.65293e+06 [24] valid_0's l1: 1.65676e+06 [25] valid_0's l1: 1.6567e+06 [26] valid_0's l1: 1.65246e+06 [27] valid_0's l1: 1.65489e+06 [28] valid_0's l1: 1.6542e+06 [29] valid_0's l1: 1.65756e+06 [30] valid_0's l1: 1.64798e+06 [31] valid_0's l1: 1.64311e+06 [32] valid_0's l1: 1.64597e+06 [33] valid_0's l1: 1.6487e+06 [34] valid_0's l1: 1.65293e+06 [35] valid_0's l1: 1.65345e+06 [36] valid_0's l1: 1.65514e+06 [37] valid_0's l1: 1.65351e+06 [38] valid_0's l1: 1.65272e+06 [39] valid_0's l1: 1.65673e+06 [40] valid_0's l1: 1.65663e+06 [41] valid_0's l1: 1.65784e+06 [42] valid_0's l1: 1.65672e+06 [43] valid_0's l1: 1.65752e+06 [44] valid_0's l1: 1.65753e+06 [45] valid_0's l1: 1.65528e+06 [46] valid_0's l1: 1.66035e+06 [47] valid_0's l1: 1.66167e+06 [48] valid_0's l1: 1.66245e+06 [49] valid_0's l1: 1.66084e+06 [50] valid_0's l1: 1.66428e+06 [51] valid_0's l1: 1.66446e+06 [52] valid_0's l1: 1.66727e+06 [53] valid_0's l1: 1.67062e+06 [54] valid_0's l1: 1.67354e+06 [55] valid_0's l1: 1.67543e+06 [56] valid_0's l1: 1.67659e+06 [57] valid_0's l1: 1.67716e+06 [58] valid_0's l1: 1.67859e+06 [59] valid_0's l1: 1.68064e+06 [60] valid_0's l1: 1.68223e+06 [61] valid_0's l1: 1.68239e+06 Early stopping, best iteration is: [31] valid_0's l1: 1.64311e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.67303e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.15957e+06 [3] valid_0's l1: 2.74986e+06 [4] valid_0's l1: 2.45151e+06 [5] valid_0's l1: 2.23013e+06 [6] valid_0's l1: 2.15413e+06 [7] valid_0's l1: 2.08852e+06 [8] valid_0's l1: 1.97426e+06 [9] valid_0's l1: 1.88784e+06 [10] valid_0's l1: 1.82639e+06 [11] valid_0's l1: 1.77641e+06 [12] valid_0's l1: 1.7611e+06 [13] valid_0's l1: 1.7281e+06 [14] valid_0's l1: 1.71859e+06 [15] valid_0's l1: 1.69797e+06 [16] valid_0's l1: 1.68329e+06 [17] valid_0's l1: 1.68038e+06 [18] valid_0's l1: 1.66907e+06 [19] valid_0's l1: 1.66621e+06 [20] valid_0's l1: 1.66287e+06 [21] valid_0's l1: 1.66018e+06 [22] valid_0's l1: 1.66398e+06 [23] valid_0's l1: 1.66027e+06 [24] valid_0's l1: 1.66435e+06 [25] valid_0's l1: 1.65992e+06 [26] valid_0's l1: 1.66286e+06 [27] valid_0's l1: 1.65973e+06 [28] valid_0's l1: 1.65974e+06 [29] valid_0's l1: 1.6571e+06 [30] valid_0's l1: 1.65502e+06 [31] valid_0's l1: 1.65876e+06 [32] valid_0's l1: 1.66014e+06 [33] valid_0's l1: 1.6561e+06 [34] valid_0's l1: 1.65872e+06 [35] valid_0's l1: 1.65888e+06 [36] valid_0's l1: 1.65659e+06 [37] valid_0's l1: 1.65984e+06 [38] valid_0's l1: 1.65935e+06 [39] valid_0's l1: 1.6611e+06 [40] valid_0's l1: 1.66236e+06 [41] valid_0's l1: 1.65998e+06 [42] valid_0's l1: 1.66392e+06 [43] valid_0's l1: 1.66179e+06 [44] valid_0's l1: 1.66272e+06 [45] valid_0's l1: 1.66274e+06 [46] valid_0's l1: 1.66192e+06 [47] valid_0's l1: 1.66486e+06 [48] valid_0's l1: 1.66293e+06 [49] valid_0's l1: 1.6636e+06 [50] valid_0's l1: 1.66386e+06 [51] valid_0's l1: 1.66824e+06 [52] valid_0's l1: 1.66677e+06 [53] valid_0's l1: 1.67165e+06 [54] valid_0's l1: 1.67538e+06 [55] valid_0's l1: 1.67742e+06 [56] valid_0's l1: 1.67659e+06 [57] valid_0's l1: 1.67701e+06 [58] valid_0's l1: 1.67817e+06 [59] valid_0's l1: 1.67647e+06 [60] valid_0's l1: 1.67678e+06 Early stopping, best iteration is: [30] valid_0's l1: 1.65502e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.74198e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.17925e+06 [3] valid_0's l1: 2.75493e+06 [4] valid_0's l1: 2.43331e+06 [5] valid_0's l1: 2.21332e+06 [6] valid_0's l1: 2.1413e+06 [7] valid_0's l1: 2.09055e+06 [8] valid_0's l1: 1.97663e+06 [9] valid_0's l1: 1.88299e+06 [10] valid_0's l1: 1.81844e+06 [11] valid_0's l1: 1.76375e+06 [12] valid_0's l1: 1.74997e+06 [13] valid_0's l1: 1.71063e+06 [14] valid_0's l1: 1.70095e+06 [15] valid_0's l1: 1.68101e+06 [16] valid_0's l1: 1.6596e+06 [17] valid_0's l1: 1.65919e+06 [18] valid_0's l1: 1.64736e+06 [19] valid_0's l1: 1.64521e+06 [20] valid_0's l1: 1.64499e+06 [21] valid_0's l1: 1.63518e+06 [22] valid_0's l1: 1.63929e+06 [23] valid_0's l1: 1.64545e+06 [24] valid_0's l1: 1.64718e+06 [25] valid_0's l1: 1.64141e+06 [26] valid_0's l1: 1.64456e+06 [27] valid_0's l1: 1.64418e+06 [28] valid_0's l1: 1.64485e+06 [29] valid_0's l1: 1.65057e+06 [30] valid_0's l1: 1.65105e+06 [31] valid_0's l1: 1.65194e+06 [32] valid_0's l1: 1.65609e+06 [33] valid_0's l1: 1.65301e+06 [34] valid_0's l1: 1.65495e+06 [35] valid_0's l1: 1.65622e+06 [36] valid_0's l1: 1.66002e+06 [37] valid_0's l1: 1.66264e+06 [38] valid_0's l1: 1.66355e+06 [39] valid_0's l1: 1.66487e+06 [40] valid_0's l1: 1.66699e+06 [41] valid_0's l1: 1.66554e+06 [42] valid_0's l1: 1.66476e+06 [43] valid_0's l1: 1.66452e+06 [44] valid_0's l1: 1.66608e+06 [45] valid_0's l1: 1.66929e+06 [46] valid_0's l1: 1.66908e+06 [47] valid_0's l1: 1.66932e+06 [48] valid_0's l1: 1.66901e+06 [49] valid_0's l1: 1.66908e+06 [50] valid_0's l1: 1.67006e+06 [51] valid_0's l1: 1.66873e+06 Early stopping, best iteration is: [21] valid_0's l1: 1.63518e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.7172e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.17345e+06 [3] valid_0's l1: 2.77403e+06 [4] valid_0's l1: 2.47886e+06 [5] valid_0's l1: 2.26288e+06 [6] valid_0's l1: 2.19668e+06 [7] valid_0's l1: 2.14756e+06 [8] valid_0's l1: 2.00628e+06 [9] valid_0's l1: 1.90309e+06 [10] valid_0's l1: 1.84191e+06 [11] valid_0's l1: 1.78333e+06 [12] valid_0's l1: 1.75664e+06 [13] valid_0's l1: 1.72714e+06 [14] valid_0's l1: 1.72713e+06 [15] valid_0's l1: 1.70508e+06 [16] valid_0's l1: 1.68751e+06 [17] valid_0's l1: 1.67159e+06 [18] valid_0's l1: 1.66508e+06 [19] valid_0's l1: 1.66682e+06 [20] valid_0's l1: 1.65486e+06 [21] valid_0's l1: 1.65807e+06 [22] valid_0's l1: 1.65513e+06 [23] valid_0's l1: 1.65293e+06 [24] valid_0's l1: 1.65676e+06 [25] valid_0's l1: 1.6567e+06 [26] valid_0's l1: 1.65246e+06 [27] valid_0's l1: 1.65489e+06 [28] valid_0's l1: 1.6542e+06 [29] valid_0's l1: 1.65756e+06 [30] valid_0's l1: 1.64798e+06 [31] valid_0's l1: 1.64311e+06 [32] valid_0's l1: 1.64597e+06 [33] valid_0's l1: 1.6487e+06 [34] valid_0's l1: 1.65293e+06 [35] valid_0's l1: 1.65345e+06 [36] valid_0's l1: 1.65514e+06 [37] valid_0's l1: 1.65351e+06 [38] valid_0's l1: 1.65272e+06 [39] valid_0's l1: 1.65673e+06 [40] valid_0's l1: 1.65663e+06 [41] valid_0's l1: 1.65784e+06 [42] valid_0's l1: 1.65672e+06 [43] valid_0's l1: 1.65752e+06 [44] valid_0's l1: 1.65753e+06 [45] valid_0's l1: 1.65528e+06 [46] valid_0's l1: 1.66035e+06 [47] valid_0's l1: 1.66167e+06 [48] valid_0's l1: 1.66245e+06 [49] valid_0's l1: 1.66084e+06 [50] valid_0's l1: 1.66428e+06 [51] valid_0's l1: 1.66446e+06 [52] valid_0's l1: 1.66727e+06 [53] valid_0's l1: 1.67062e+06 [54] valid_0's l1: 1.67354e+06 [55] valid_0's l1: 1.67543e+06 [56] valid_0's l1: 1.67659e+06 [57] valid_0's l1: 1.67716e+06 [58] valid_0's l1: 1.67859e+06 [59] valid_0's l1: 1.68064e+06 [60] valid_0's l1: 1.68223e+06 [61] valid_0's l1: 1.68239e+06 Early stopping, best iteration is: [31] valid_0's l1: 1.64311e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.67303e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.15957e+06 [3] valid_0's l1: 2.74986e+06 [4] valid_0's l1: 2.45151e+06 [5] valid_0's l1: 2.23013e+06 [6] valid_0's l1: 2.15413e+06 [7] valid_0's l1: 2.08852e+06 [8] valid_0's l1: 1.97426e+06 [9] valid_0's l1: 1.88784e+06 [10] valid_0's l1: 1.82639e+06 [11] valid_0's l1: 1.77641e+06 [12] valid_0's l1: 1.7611e+06 [13] valid_0's l1: 1.7281e+06 [14] valid_0's l1: 1.71859e+06 [15] valid_0's l1: 1.69797e+06 [16] valid_0's l1: 1.68329e+06 [17] valid_0's l1: 1.68038e+06 [18] valid_0's l1: 1.66907e+06 [19] valid_0's l1: 1.66621e+06 [20] valid_0's l1: 1.66287e+06 [21] valid_0's l1: 1.66018e+06 [22] valid_0's l1: 1.66398e+06 [23] valid_0's l1: 1.66027e+06 [24] valid_0's l1: 1.66435e+06 [25] valid_0's l1: 1.65992e+06 [26] valid_0's l1: 1.66286e+06 [27] valid_0's l1: 1.65973e+06 [28] valid_0's l1: 1.65974e+06 [29] valid_0's l1: 1.6571e+06 [30] valid_0's l1: 1.65502e+06 [31] valid_0's l1: 1.65876e+06 [32] valid_0's l1: 1.66014e+06 [33] valid_0's l1: 1.6561e+06 [34] valid_0's l1: 1.65872e+06 [35] valid_0's l1: 1.65888e+06 [36] valid_0's l1: 1.65659e+06 [37] valid_0's l1: 1.65984e+06 [38] valid_0's l1: 1.65935e+06 [39] valid_0's l1: 1.6611e+06 [40] valid_0's l1: 1.66236e+06 [41] valid_0's l1: 1.65998e+06 [42] valid_0's l1: 1.66392e+06 [43] valid_0's l1: 1.66179e+06 [44] valid_0's l1: 1.66272e+06 [45] valid_0's l1: 1.66274e+06 [46] valid_0's l1: 1.66192e+06 [47] valid_0's l1: 1.66486e+06 [48] valid_0's l1: 1.66293e+06 [49] valid_0's l1: 1.6636e+06 [50] valid_0's l1: 1.66386e+06 [51] valid_0's l1: 1.66824e+06 [52] valid_0's l1: 1.66677e+06 [53] valid_0's l1: 1.67165e+06 [54] valid_0's l1: 1.67538e+06 [55] valid_0's l1: 1.67742e+06 [56] valid_0's l1: 1.67659e+06 [57] valid_0's l1: 1.67701e+06 [58] valid_0's l1: 1.67817e+06 [59] valid_0's l1: 1.67647e+06 [60] valid_0's l1: 1.67678e+06 Early stopping, best iteration is: [30] valid_0's l1: 1.65502e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.74198e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.17925e+06 [3] valid_0's l1: 2.75493e+06 [4] valid_0's l1: 2.43331e+06 [5] valid_0's l1: 2.21332e+06 [6] valid_0's l1: 2.1413e+06 [7] valid_0's l1: 2.09055e+06 [8] valid_0's l1: 1.97663e+06 [9] valid_0's l1: 1.88299e+06 [10] valid_0's l1: 1.81844e+06 [11] valid_0's l1: 1.76375e+06 [12] valid_0's l1: 1.74997e+06 [13] valid_0's l1: 1.71063e+06 [14] valid_0's l1: 1.70095e+06 [15] valid_0's l1: 1.68101e+06 [16] valid_0's l1: 1.6596e+06 [17] valid_0's l1: 1.65919e+06 [18] valid_0's l1: 1.64736e+06 [19] valid_0's l1: 1.64521e+06 [20] valid_0's l1: 1.64499e+06 [21] valid_0's l1: 1.63518e+06 [22] valid_0's l1: 1.63929e+06 [23] valid_0's l1: 1.64545e+06 [24] valid_0's l1: 1.64718e+06 [25] valid_0's l1: 1.64141e+06 [26] valid_0's l1: 1.64456e+06 [27] valid_0's l1: 1.64418e+06 [28] valid_0's l1: 1.64485e+06 [29] valid_0's l1: 1.65057e+06 [30] valid_0's l1: 1.65105e+06 [31] valid_0's l1: 1.65194e+06 [32] valid_0's l1: 1.65609e+06 [33] valid_0's l1: 1.65301e+06 [34] valid_0's l1: 1.65495e+06 [35] valid_0's l1: 1.65622e+06 [36] valid_0's l1: 1.66002e+06 [37] valid_0's l1: 1.66264e+06 [38] valid_0's l1: 1.66355e+06 [39] valid_0's l1: 1.66487e+06 [40] valid_0's l1: 1.66699e+06 [41] valid_0's l1: 1.66554e+06 [42] valid_0's l1: 1.66476e+06 [43] valid_0's l1: 1.66452e+06 [44] valid_0's l1: 1.66608e+06 [45] valid_0's l1: 1.66929e+06 [46] valid_0's l1: 1.66908e+06 [47] valid_0's l1: 1.66932e+06 [48] valid_0's l1: 1.66901e+06 [49] valid_0's l1: 1.66908e+06 [50] valid_0's l1: 1.67006e+06 [51] valid_0's l1: 1.66873e+06 Early stopping, best iteration is: [21] valid_0's l1: 1.63518e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.7172e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.17345e+06 [3] valid_0's l1: 2.77403e+06 [4] valid_0's l1: 2.47886e+06 [5] valid_0's l1: 2.26288e+06 [6] valid_0's l1: 2.19668e+06 [7] valid_0's l1: 2.14756e+06 [8] valid_0's l1: 2.00628e+06 [9] valid_0's l1: 1.90309e+06 [10] valid_0's l1: 1.84191e+06 [11] valid_0's l1: 1.78333e+06 [12] valid_0's l1: 1.75664e+06 [13] valid_0's l1: 1.72714e+06 [14] valid_0's l1: 1.72713e+06 [15] valid_0's l1: 1.70508e+06 [16] valid_0's l1: 1.68751e+06 [17] valid_0's l1: 1.67159e+06 [18] valid_0's l1: 1.66508e+06 [19] valid_0's l1: 1.66682e+06 [20] valid_0's l1: 1.65486e+06 [21] valid_0's l1: 1.65807e+06 [22] valid_0's l1: 1.65513e+06 [23] valid_0's l1: 1.65293e+06 [24] valid_0's l1: 1.65676e+06 [25] valid_0's l1: 1.6567e+06 [26] valid_0's l1: 1.65246e+06 [27] valid_0's l1: 1.65489e+06 [28] valid_0's l1: 1.6542e+06 [29] valid_0's l1: 1.65756e+06 [30] valid_0's l1: 1.64798e+06 [31] valid_0's l1: 1.64311e+06 [32] valid_0's l1: 1.64597e+06 [33] valid_0's l1: 1.6487e+06 [34] valid_0's l1: 1.65293e+06 [35] valid_0's l1: 1.65345e+06 [36] valid_0's l1: 1.65514e+06 [37] valid_0's l1: 1.65351e+06 [38] valid_0's l1: 1.65272e+06 [39] valid_0's l1: 1.65673e+06 [40] valid_0's l1: 1.65663e+06 [41] valid_0's l1: 1.65784e+06 [42] valid_0's l1: 1.65672e+06 [43] valid_0's l1: 1.65752e+06 [44] valid_0's l1: 1.65753e+06 [45] valid_0's l1: 1.65528e+06 [46] valid_0's l1: 1.66035e+06 [47] valid_0's l1: 1.66167e+06 [48] valid_0's l1: 1.66245e+06 [49] valid_0's l1: 1.66084e+06 [50] valid_0's l1: 1.66428e+06 [51] valid_0's l1: 1.66446e+06 [52] valid_0's l1: 1.66727e+06 [53] valid_0's l1: 1.67062e+06 [54] valid_0's l1: 1.67354e+06 [55] valid_0's l1: 1.67543e+06 [56] valid_0's l1: 1.67659e+06 [57] valid_0's l1: 1.67716e+06 [58] valid_0's l1: 1.67859e+06 [59] valid_0's l1: 1.68064e+06 [60] valid_0's l1: 1.68223e+06 [61] valid_0's l1: 1.68239e+06 Early stopping, best iteration is: [31] valid_0's l1: 1.64311e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.67303e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.15957e+06 [3] valid_0's l1: 2.74986e+06 [4] valid_0's l1: 2.45151e+06 [5] valid_0's l1: 2.23013e+06 [6] valid_0's l1: 2.15413e+06 [7] valid_0's l1: 2.08852e+06 [8] valid_0's l1: 1.97426e+06 [9] valid_0's l1: 1.88784e+06 [10] valid_0's l1: 1.82639e+06 [11] valid_0's l1: 1.77641e+06 [12] valid_0's l1: 1.7611e+06 [13] valid_0's l1: 1.7281e+06 [14] valid_0's l1: 1.71859e+06 [15] valid_0's l1: 1.69797e+06 [16] valid_0's l1: 1.68329e+06 [17] valid_0's l1: 1.68038e+06 [18] valid_0's l1: 1.66907e+06 [19] valid_0's l1: 1.66621e+06 [20] valid_0's l1: 1.66287e+06 [21] valid_0's l1: 1.66018e+06 [22] valid_0's l1: 1.66398e+06 [23] valid_0's l1: 1.66027e+06 [24] valid_0's l1: 1.66435e+06 [25] valid_0's l1: 1.65992e+06 [26] valid_0's l1: 1.66286e+06 [27] valid_0's l1: 1.65973e+06 [28] valid_0's l1: 1.65974e+06 [29] valid_0's l1: 1.6571e+06 [30] valid_0's l1: 1.65502e+06 [31] valid_0's l1: 1.65876e+06 [32] valid_0's l1: 1.66014e+06 [33] valid_0's l1: 1.6561e+06 [34] valid_0's l1: 1.65872e+06 [35] valid_0's l1: 1.65888e+06 [36] valid_0's l1: 1.65659e+06 [37] valid_0's l1: 1.65984e+06 [38] valid_0's l1: 1.65935e+06 [39] valid_0's l1: 1.6611e+06 [40] valid_0's l1: 1.66236e+06 [41] valid_0's l1: 1.65998e+06 [42] valid_0's l1: 1.66392e+06 [43] valid_0's l1: 1.66179e+06 [44] valid_0's l1: 1.66272e+06 [45] valid_0's l1: 1.66274e+06 [46] valid_0's l1: 1.66192e+06 [47] valid_0's l1: 1.66486e+06 [48] valid_0's l1: 1.66293e+06 [49] valid_0's l1: 1.6636e+06 [50] valid_0's l1: 1.66386e+06 [51] valid_0's l1: 1.66824e+06 [52] valid_0's l1: 1.66677e+06 [53] valid_0's l1: 1.67165e+06 [54] valid_0's l1: 1.67538e+06 [55] valid_0's l1: 1.67742e+06 [56] valid_0's l1: 1.67659e+06 [57] valid_0's l1: 1.67701e+06 [58] valid_0's l1: 1.67817e+06 [59] valid_0's l1: 1.67647e+06 [60] valid_0's l1: 1.67678e+06 Early stopping, best iteration is: [30] valid_0's l1: 1.65502e+06 [LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.74198e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.17925e+06 [3] valid_0's l1: 2.75493e+06 [4] valid_0's l1: 2.43331e+06 [5] valid_0's l1: 2.21332e+06 [6] valid_0's l1: 2.1413e+06 [7] valid_0's l1: 2.09055e+06 [8] valid_0's l1: 1.97663e+06 [9] valid_0's l1: 1.88299e+06 [10] valid_0's l1: 1.81844e+06 [11] valid_0's l1: 1.76375e+06 [12] valid_0's l1: 1.74997e+06 [13] valid_0's l1: 1.71063e+06 [14] valid_0's l1: 1.70095e+06 [15] valid_0's l1: 1.68101e+06 [16] valid_0's l1: 1.6596e+06 [17] valid_0's l1: 1.65919e+06 [18] valid_0's l1: 1.64736e+06 [19] valid_0's l1: 1.64521e+06 [20] valid_0's l1: 1.64499e+06 [21] valid_0's l1: 1.63518e+06 [22] valid_0's l1: 1.63929e+06 [23] valid_0's l1: 1.64545e+06 [24] valid_0's l1: 1.64718e+06 [25] valid_0's l1: 1.64141e+06 [26] valid_0's l1: 1.64456e+06 [27] valid_0's l1: 1.64418e+06 [28] valid_0's l1: 1.64485e+06 [29] valid_0's l1: 1.65057e+06 [30] valid_0's l1: 1.65105e+06 [31] valid_0's l1: 1.65194e+06 [32] valid_0's l1: 1.65609e+06 [33] valid_0's l1: 1.65301e+06 [34] valid_0's l1: 1.65495e+06 [35] valid_0's l1: 1.65622e+06 [36] valid_0's l1: 1.66002e+06 [37] valid_0's l1: 1.66264e+06 [38] valid_0's l1: 1.66355e+06 [39] valid_0's l1: 1.66487e+06 [40] valid_0's l1: 1.66699e+06 [41] valid_0's l1: 1.66554e+06 [42] valid_0's l1: 1.66476e+06 [43] valid_0's l1: 1.66452e+06 [44] valid_0's l1: 1.66608e+06 [45] valid_0's l1: 1.66929e+06 [46] valid_0's l1: 1.66908e+06 [47] valid_0's l1: 1.66932e+06 [48] valid_0's l1: 1.66901e+06 [49] valid_0's l1: 1.66908e+06 [50] valid_0's l1: 1.67006e+06 [51] valid_0's l1: 1.66873e+06 Early stopping, best iteration is: [21] valid_0's l1: 1.63518e+06
[Parallel(n_jobs=1)]: Done 108 out of 108 | elapsed: 2.6min finished C:\Users\YAVUZ\anaconda3\lib\site-packages\sklearn\model_selection\_search.py:847: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.
[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8 [LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8 [1] valid_0's l1: 3.86831e+06 Training until validation scores don't improve for 30 rounds [2] valid_0's l1: 3.56444e+06 [3] valid_0's l1: 3.29847e+06 [4] valid_0's l1: 3.0627e+06 [5] valid_0's l1: 2.85804e+06 [6] valid_0's l1: 2.75834e+06 [7] valid_0's l1: 2.6784e+06 [8] valid_0's l1: 2.52683e+06 [9] valid_0's l1: 2.3987e+06 [10] valid_0's l1: 2.28371e+06 [11] valid_0's l1: 2.18641e+06 [12] valid_0's l1: 2.13955e+06 [13] valid_0's l1: 2.06087e+06 [14] valid_0's l1: 2.03278e+06 [15] valid_0's l1: 1.96801e+06 [16] valid_0's l1: 1.90775e+06 [17] valid_0's l1: 1.86176e+06 [18] valid_0's l1: 1.82014e+06 [19] valid_0's l1: 1.78398e+06 [20] valid_0's l1: 1.75302e+06 [21] valid_0's l1: 1.72988e+06 [22] valid_0's l1: 1.7051e+06 [23] valid_0's l1: 1.68279e+06 [24] valid_0's l1: 1.66601e+06 [25] valid_0's l1: 1.65001e+06 [26] valid_0's l1: 1.6369e+06 [27] valid_0's l1: 1.62504e+06 [28] valid_0's l1: 1.61374e+06 [29] valid_0's l1: 1.60745e+06 [30] valid_0's l1: 1.60007e+06 [31] valid_0's l1: 1.59604e+06 [32] valid_0's l1: 1.58828e+06 [33] valid_0's l1: 1.58689e+06 [34] valid_0's l1: 1.58121e+06 [35] valid_0's l1: 1.57792e+06 [36] valid_0's l1: 1.57232e+06 [37] valid_0's l1: 1.57041e+06 [38] valid_0's l1: 1.56866e+06 [39] valid_0's l1: 1.56406e+06 [40] valid_0's l1: 1.56267e+06 [41] valid_0's l1: 1.56254e+06 [42] valid_0's l1: 1.5593e+06 [43] valid_0's l1: 1.55895e+06 [44] valid_0's l1: 1.55912e+06 [45] valid_0's l1: 1.55571e+06 [46] valid_0's l1: 1.55615e+06 [47] valid_0's l1: 1.55678e+06 [48] valid_0's l1: 1.5567e+06 [49] valid_0's l1: 1.55792e+06 [50] valid_0's l1: 1.55928e+06 [51] valid_0's l1: 1.5584e+06 [52] valid_0's l1: 1.55888e+06 [53] valid_0's l1: 1.5584e+06 [54] valid_0's l1: 1.55833e+06 [55] valid_0's l1: 1.55763e+06 [56] valid_0's l1: 1.55671e+06 [57] valid_0's l1: 1.55731e+06 [58] valid_0's l1: 1.55619e+06 [59] valid_0's l1: 1.55783e+06 [60] valid_0's l1: 1.55753e+06 [61] valid_0's l1: 1.55744e+06 [62] valid_0's l1: 1.55883e+06 [63] valid_0's l1: 1.55826e+06 [64] valid_0's l1: 1.55896e+06 [65] valid_0's l1: 1.56e+06 [66] valid_0's l1: 1.55806e+06 [67] valid_0's l1: 1.5583e+06 [68] valid_0's l1: 1.55702e+06 [69] valid_0's l1: 1.5565e+06 [70] valid_0's l1: 1.55543e+06 [71] valid_0's l1: 1.55591e+06 [72] valid_0's l1: 1.55505e+06 [73] valid_0's l1: 1.55459e+06 [74] valid_0's l1: 1.55404e+06 [75] valid_0's l1: 1.55336e+06 [76] valid_0's l1: 1.55492e+06 [77] valid_0's l1: 1.55441e+06 [78] valid_0's l1: 1.55459e+06 [79] valid_0's l1: 1.55399e+06 [80] valid_0's l1: 1.55429e+06 [81] valid_0's l1: 1.55362e+06 [82] valid_0's l1: 1.55502e+06 [83] valid_0's l1: 1.55553e+06 [84] valid_0's l1: 1.55603e+06 [85] valid_0's l1: 1.55555e+06 [86] valid_0's l1: 1.55543e+06 [87] valid_0's l1: 1.5554e+06 [88] valid_0's l1: 1.556e+06 [89] valid_0's l1: 1.55712e+06 [90] valid_0's l1: 1.55612e+06 [91] valid_0's l1: 1.55725e+06 [92] valid_0's l1: 1.55686e+06 [93] valid_0's l1: 1.55708e+06 [94] valid_0's l1: 1.55698e+06 [95] valid_0's l1: 1.55728e+06 [96] valid_0's l1: 1.55628e+06 [97] valid_0's l1: 1.55569e+06 [98] valid_0's l1: 1.55562e+06 [99] valid_0's l1: 1.5572e+06 [100] valid_0's l1: 1.55752e+06 [101] valid_0's l1: 1.55841e+06 [102] valid_0's l1: 1.55924e+06 [103] valid_0's l1: 1.55884e+06 [104] valid_0's l1: 1.55992e+06 [105] valid_0's l1: 1.55944e+06 Early stopping, best iteration is: [75] valid_0's l1: 1.55336e+06
GridSearchCV(cv=3,
estimator=LGBMRegressor(bagging_fraction=0.8, feature_fraction=0.8,
metric='mae', n_estimators=400,
objective='regression', reg_lambda=0.9),
iid=False, n_jobs=1,
param_grid={'learning_rate': [0.1, 0.2], 'max_depth': [-1, 5, 6],
'n_estimators': [400, 800, 1200],
'num_leaves': [100, 200]},
scoring='neg_mean_absolute_error', verbose=1)
gridSearchCV.best_params_, gridSearchCV.best_score_
({'learning_rate': 0.1,
'max_depth': 6,
'n_estimators': 400,
'num_leaves': 100},
-1713679.684827206)
gridSearchCV.cv_results_
{'mean_fit_time': array([2.3932662 , 3.23434997, 2.32777556, 3.09804734, 2.42152341,
3.35436273, 0.8301127 , 0.83908947, 0.82147058, 0.8370947 ,
0.82944957, 0.85437973, 0.95444759, 0.98835635, 1.03124221,
0.89327717, 0.88097731, 0.88430134, 1.74333747, 2.38329228,
1.69347024, 2.3965896 , 1.70510642, 2.34838597, 0.65624475,
0.66256094, 0.66733479, 0.65458353, 0.65890511, 0.69181665,
0.71176354, 0.71974134, 0.70611103, 0.7094357 , 0.70079255,
0.71541897]),
'std_fit_time': array([0.03092722, 0.26178615, 0.07828097, 0.20179714, 0.07059756,
0.38703346, 0.0344082 , 0.09609417, 0.06700301, 0.06578449,
0.05880252, 0.05896218, 0.09833752, 0.03546882, 0.04562376,
0.06958622, 0.06108109, 0.03320003, 0.02973127, 0.02206609,
0.04531787, 0.06538363, 0.04731745, 0.06895418, 0.02372722,
0.02545794, 0.02495317, 0.02646674, 0.02315212, 0.01506651,
0.03373644, 0.02631581, 0.02016165, 0.0195495 , 0.0236905 ,
0.02578144]),
'mean_score_time': array([0.08311057, 0.09108941, 0.09574254, 0.09109028, 0.07147598,
0.08311025, 0.08577061, 0.08510621, 0.08211342, 0.08843048,
0.09042374, 0.09075745, 0.08111684, 0.0591832 , 0.08344261,
0.09175499, 0.08776522, 0.08842977, 0.08710027, 0.08843017,
0.08543785, 0.09374984, 0.08709955, 0.0927527 , 0.0874327 ,
0.0824465 , 0.09009202, 0.07346932, 0.08709939, 0.07513173,
0.08710011, 0.080784 , 0.09208687, 0.08543841, 0.08543777,
0.08277885]),
'std_score_time': array([0.00588976, 0.00261774, 0.01058427, 0.00417813, 0.00600197,
0.01879013, 0.0088085 , 0.01235916, 0.00248729, 0.001882 ,
0.00235016, 0.0008143 , 0.01018219, 0.01180485, 0.00823781,
0.00325641, 0.00282064, 0.0016947 , 0.00204936, 0.0024884 ,
0.00094083, 0.0021545 , 0.0036714 , 0.00373164, 0.00046991,
0.00477137, 0.00285933, 0.00702041, 0.00409853, 0.01093504,
0.00621884, 0.01139998, 0.00169486, 0.00367217, 0.00401719,
0.01139989]),
'param_learning_rate': masked_array(data=[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.2,
0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
0.2, 0.2, 0.2],
mask=[False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False],
fill_value='?',
dtype=object),
'param_max_depth': masked_array(data=[-1, -1, -1, -1, -1, -1, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6,
6, 6, -1, -1, -1, -1, -1, -1, 5, 5, 5, 5, 5, 5, 6, 6,
6, 6, 6, 6],
mask=[False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False],
fill_value='?',
dtype=object),
'param_n_estimators': masked_array(data=[400, 400, 800, 800, 1200, 1200, 400, 400, 800, 800,
1200, 1200, 400, 400, 800, 800, 1200, 1200, 400, 400,
800, 800, 1200, 1200, 400, 400, 800, 800, 1200, 1200,
400, 400, 800, 800, 1200, 1200],
mask=[False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False],
fill_value='?',
dtype=object),
'param_num_leaves': masked_array(data=[100, 200, 100, 200, 100, 200, 100, 200, 100, 200, 100,
200, 100, 200, 100, 200, 100, 200, 100, 200, 100, 200,
100, 200, 100, 200, 100, 200, 100, 200, 100, 200, 100,
200, 100, 200],
mask=[False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False],
fill_value='?',
dtype=object),
'params': [{'learning_rate': 0.1,
'max_depth': -1,
'n_estimators': 400,
'num_leaves': 100},
{'learning_rate': 0.1,
'max_depth': -1,
'n_estimators': 400,
'num_leaves': 200},
{'learning_rate': 0.1,
'max_depth': -1,
'n_estimators': 800,
'num_leaves': 100},
{'learning_rate': 0.1,
'max_depth': -1,
'n_estimators': 800,
'num_leaves': 200},
{'learning_rate': 0.1,
'max_depth': -1,
'n_estimators': 1200,
'num_leaves': 100},
{'learning_rate': 0.1,
'max_depth': -1,
'n_estimators': 1200,
'num_leaves': 200},
{'learning_rate': 0.1,
'max_depth': 5,
'n_estimators': 400,
'num_leaves': 100},
{'learning_rate': 0.1,
'max_depth': 5,
'n_estimators': 400,
'num_leaves': 200},
{'learning_rate': 0.1,
'max_depth': 5,
'n_estimators': 800,
'num_leaves': 100},
{'learning_rate': 0.1,
'max_depth': 5,
'n_estimators': 800,
'num_leaves': 200},
{'learning_rate': 0.1,
'max_depth': 5,
'n_estimators': 1200,
'num_leaves': 100},
{'learning_rate': 0.1,
'max_depth': 5,
'n_estimators': 1200,
'num_leaves': 200},
{'learning_rate': 0.1,
'max_depth': 6,
'n_estimators': 400,
'num_leaves': 100},
{'learning_rate': 0.1,
'max_depth': 6,
'n_estimators': 400,
'num_leaves': 200},
{'learning_rate': 0.1,
'max_depth': 6,
'n_estimators': 800,
'num_leaves': 100},
{'learning_rate': 0.1,
'max_depth': 6,
'n_estimators': 800,
'num_leaves': 200},
{'learning_rate': 0.1,
'max_depth': 6,
'n_estimators': 1200,
'num_leaves': 100},
{'learning_rate': 0.1,
'max_depth': 6,
'n_estimators': 1200,
'num_leaves': 200},
{'learning_rate': 0.2,
'max_depth': -1,
'n_estimators': 400,
'num_leaves': 100},
{'learning_rate': 0.2,
'max_depth': -1,
'n_estimators': 400,
'num_leaves': 200},
{'learning_rate': 0.2,
'max_depth': -1,
'n_estimators': 800,
'num_leaves': 100},
{'learning_rate': 0.2,
'max_depth': -1,
'n_estimators': 800,
'num_leaves': 200},
{'learning_rate': 0.2,
'max_depth': -1,
'n_estimators': 1200,
'num_leaves': 100},
{'learning_rate': 0.2,
'max_depth': -1,
'n_estimators': 1200,
'num_leaves': 200},
{'learning_rate': 0.2,
'max_depth': 5,
'n_estimators': 400,
'num_leaves': 100},
{'learning_rate': 0.2,
'max_depth': 5,
'n_estimators': 400,
'num_leaves': 200},
{'learning_rate': 0.2,
'max_depth': 5,
'n_estimators': 800,
'num_leaves': 100},
{'learning_rate': 0.2,
'max_depth': 5,
'n_estimators': 800,
'num_leaves': 200},
{'learning_rate': 0.2,
'max_depth': 5,
'n_estimators': 1200,
'num_leaves': 100},
{'learning_rate': 0.2,
'max_depth': 5,
'n_estimators': 1200,
'num_leaves': 200},
{'learning_rate': 0.2,
'max_depth': 6,
'n_estimators': 400,
'num_leaves': 100},
{'learning_rate': 0.2,
'max_depth': 6,
'n_estimators': 400,
'num_leaves': 200},
{'learning_rate': 0.2,
'max_depth': 6,
'n_estimators': 800,
'num_leaves': 100},
{'learning_rate': 0.2,
'max_depth': 6,
'n_estimators': 800,
'num_leaves': 200},
{'learning_rate': 0.2,
'max_depth': 6,
'n_estimators': 1200,
'num_leaves': 100},
{'learning_rate': 0.2,
'max_depth': 6,
'n_estimators': 1200,
'num_leaves': 200}],
'split0_test_score': array([-1708596.67757451, -1739473.27619857, -1708596.67757451,
-1739473.27619857, -1708596.67757451, -1739473.27619857,
-1703987.76040836, -1703987.76040836, -1703987.76040836,
-1703987.76040836, -1703987.76040836, -1703987.76040836,
-1700057.52596791, -1700057.52596791, -1700057.52596791,
-1700057.52596791, -1700057.52596791, -1700057.52596791,
-1814733.17601571, -1827893.6066538 , -1814733.17601571,
-1827893.6066538 , -1814733.17601571, -1827893.6066538 ,
-1740442.47062728, -1740442.47062728, -1740442.47062728,
-1740442.47062728, -1740442.47062728, -1740442.47062728,
-1739532.69179785, -1739532.69179785, -1739532.69179785,
-1739532.69179785, -1739532.69179785, -1739532.69179785]),
'split1_test_score': array([-1790858.56377364, -1780182.58900775, -1790858.56377364,
-1780182.58900775, -1790858.56377364, -1780182.58900775,
-1751622.93357624, -1751622.93357624, -1751622.93357624,
-1751622.93357624, -1751622.93357624, -1751622.93357624,
-1760564.16450841, -1760564.16450841, -1760564.16450841,
-1760564.16450841, -1760564.16450841, -1760564.16450841,
-1890928.97641883, -1894299.4831432 , -1890928.97641883,
-1894299.4831432 , -1890928.97641883, -1894299.4831432 ,
-1824776.86889778, -1824776.86889778, -1824776.86889778,
-1824776.86889778, -1824776.86889778, -1824776.86889778,
-1825484.92770088, -1825484.92770088, -1825484.92770088,
-1825484.92770088, -1825484.92770088, -1825484.92770088]),
'split2_test_score': array([-1690056.3933445 , -1697198.90649058, -1690056.3933445 ,
-1697198.90649058, -1690056.3933445 , -1697198.90649058,
-1703008.2654063 , -1703008.2654063 , -1703008.2654063 ,
-1703008.2654063 , -1703008.2654063 , -1703008.2654063 ,
-1680417.3640053 , -1680417.3640053 , -1680417.3640053 ,
-1680417.3640053 , -1680417.3640053 , -1680417.3640053 ,
-1799372.90858213, -1799904.33686236, -1799372.90858213,
-1799904.33686236, -1799372.90858213, -1799904.33686236,
-1738835.56317212, -1738835.56317212, -1738835.56317212,
-1738835.56317212, -1738835.56317212, -1738835.56317212,
-1720000.85175119, -1720000.85175119, -1720000.85175119,
-1720000.85175119, -1720000.85175119, -1720000.85175119]),
'mean_test_score': array([-1729837.21156422, -1738951.59056563, -1729837.21156422,
-1738951.59056563, -1729837.21156422, -1738951.59056563,
-1719539.6531303 , -1719539.6531303 , -1719539.6531303 ,
-1719539.6531303 , -1719539.6531303 , -1719539.6531303 ,
-1713679.68482721, -1713679.68482721, -1713679.68482721,
-1713679.68482721, -1713679.68482721, -1713679.68482721,
-1835011.68700556, -1840699.14221978, -1835011.68700556,
-1840699.14221978, -1835011.68700556, -1840699.14221978,
-1768018.30089906, -1768018.30089906, -1768018.30089906,
-1768018.30089906, -1768018.30089906, -1768018.30089906,
-1761672.82374997, -1761672.82374997, -1761672.82374997,
-1761672.82374997, -1761672.82374997, -1761672.82374997]),
'std_test_score': array([43807.45449451, 33879.95482044, 43807.45449451, 33879.95482044,
43807.45449451, 33879.95482044, 22689.8290833 , 22689.8290833 ,
22689.8290833 , 22689.8290833 , 22689.8290833 , 22689.8290833 ,
34108.15949997, 34108.15949997, 34108.15949997, 34108.15949997,
34108.15949997, 34108.15949997, 40033.66829227, 39586.16943828,
40033.66829227, 39586.16943828, 40033.66829227, 39586.16943828,
40139.7294364 , 40139.7294364 , 40139.7294364 , 40139.7294364 ,
40139.7294364 , 40139.7294364 , 45821.11341498, 45821.11341498,
45821.11341498, 45821.11341498, 45821.11341498, 45821.11341498]),
'rank_test_score': array([13, 16, 13, 16, 13, 16, 7, 7, 7, 7, 7, 7, 1, 1, 1, 1, 1,
1, 31, 34, 31, 34, 31, 34, 25, 25, 25, 25, 25, 25, 19, 19, 19, 19,
19, 19])}
#Predict the model & see the model results
opt_model = gridSearchCV.best_estimator_
predictions = (model.fit(x_train, y_train)).predict(x_test)
best_predictions = opt_model.predict(x_test)
print("Default Model\n------")
print("RMSE on testing data: {:.4f}".format(sqrt(mean_squared_error(y_test, predictions))))
print("r_2 on testing data: {:.4f}".format(r2_score(y_test, predictions)))
print("MAE on testing data: {:.4f}".format(mean_absolute_error(y_test, predictions)))
print("\nGrid Best Model\n------")
print("RMSE on testing data: {:.4f}".format(sqrt(mean_squared_error(y_test, best_predictions))))
print("r_2 on testing data: {:.4f}".format(r2_score(y_test, best_predictions)))
print("MAE on testing data: {:.4f}".format(mean_absolute_error(y_test, best_predictions)))
Default Model ------ RMSE on testing data: 3726731.7812 r_2 on testing data: 0.7886 MAE on testing data: 1618545.8519 Grid Best Model ------ RMSE on testing data: 3672946.1078 r_2 on testing data: 0.7947 MAE on testing data: 1553520.0283
#To get the feature names for feature importance
X=df.drop(['Fee_Money'],axis=1)
#Feature importance
feature_imp = pd.DataFrame(sorted(zip(opt_model.feature_importances_,X.columns)), columns=['Value','Feature'])
plt.figure(figsize=(20, 20))
sns.barplot(x="Value", y="Feature", data=feature_imp.sort_values(by="Value", ascending=False))
plt.title('LightGBM Feature Importance')
plt.tight_layout()
plt.show()
plt.savefig('lgbm_importances-01.png')
<Figure size 432x288 with 0 Axes>
#Feature importance with top 15 features
feature_imp2 = pd.DataFrame(sorted(zip(opt_model.feature_importances_,X.columns)), columns=['Value','Feature'])
plt.figure(figsize=(20, 10))
a=sns.barplot(x="Value", y="Feature", data=feature_imp2.sort_values(by="Value", ascending=False).head(20))
a.set_xlabel("Value",fontsize=20)
a.set_ylabel("Feature",fontsize=20)
plt.title('LightGBM Feature Importance')
plt.tight_layout()
plt.show()
plt.savefig('lgbm_importances-02.png')
<Figure size 432x288 with 0 Axes>
# Plot prediction & Values
lineStart = y_test.min()
lineEnd = y_test.max()
plt.figure()
plt.scatter(y_test, best_predictions, color = 'k', alpha=0.5)
plt.plot([lineStart, lineEnd], [lineStart, lineEnd], 'k-', color = 'r')
plt.xlim(lineStart, lineEnd)
plt.ylim(lineStart, lineEnd)
plt.title("Predictions vs Values on test dataset")
plt.xlabel("Values")
plt.ylabel("Predictions")
plt.show()
y_test
array([8200000, 5600000, 3000000, ..., 450000, 550000, 2470000],
dtype=int64)
Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result as fast as possible is key to doing good research.
Keras is the high-level API of TensorFlow 2.0: an approchable, highly-productive interface for solving machine learning problems, with a focus on modern deep learning. It provides essential abstractions and building blocks for developing and shipping machine learning solutions with high iteration velocity.
Keras empowers engineers and researchers to take full advantage of the scalability and cross-platform capabilities of TensorFlow 2.0: you can run Keras on TPU or on large clusters of GPUs.

Source : Keras
# fill na with 0
x_train2.fillna(0,inplace=True)
x_train2 = x_train2.replace([np.inf, -np.inf],0)
x_test2.fillna(0,inplace=True)
x_test2 = x_test2.replace([np.inf, -np.inf],0)
train_label = y_train2
test_label = y_test2
train_features = x_train2
test_features = x_test2
# import the necessary packages
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Input
from tensorflow.keras.models import Model
def create_mlp(dim, regress=False):
# define our MLP network
model = Sequential()
model.add(Dense(5000, input_dim=dim, activation="relu"))
model.add(Dropout(0.2))
model.add(Dense(2500, activation="relu"))
model.add(Dropout(0.2))
model.add(Dense(1000, activation="relu"))
model.add(Dropout(0.2))
model.add(Dense(500, activation="relu"))
model.add(Dropout(0.2))
model.add(Dense(250, activation="relu"))
# check to see if the regression node should be added
if regress:
model.add(Dense(1, activation="linear"))
# return our model
return model
# # train the model
model = create_mlp(test_features.shape[1], regress=True)
model.compile(loss="mean_squared_error", optimizer='adam')
print("[INFO] training model...")
model.fit(x=train_features, y=train_label,
validation_split=0.2,
epochs=25, batch_size=100)
[INFO] training model... Epoch 1/25 54/54 [==============================] - 8s 157ms/step - loss: 35041237270528.0000 - val_loss: 33943818600448.0000 Epoch 2/25 54/54 [==============================] - 8s 155ms/step - loss: 21715289636864.0000 - val_loss: 17432988090368.0000 Epoch 3/25 54/54 [==============================] - 8s 155ms/step - loss: 24374606299136.0000 - val_loss: 18898518802432.0000 Epoch 4/25 54/54 [==============================] - 8s 155ms/step - loss: 20208630628352.0000 - val_loss: 14113268826112.0000 Epoch 5/25 54/54 [==============================] - 8s 155ms/step - loss: 21842811158528.0000 - val_loss: 14354304991232.0000 Epoch 6/25 54/54 [==============================] - 8s 156ms/step - loss: 23898152239104.0000 - val_loss: 15524380540928.0000 Epoch 7/25 54/54 [==============================] - 9s 159ms/step - loss: 28520231206912.0000 - val_loss: 20431098609664.0000 Epoch 8/25 54/54 [==============================] - 9s 158ms/step - loss: 24890497302528.0000 - val_loss: 20632569905152.0000 Epoch 9/25 54/54 [==============================] - 8s 156ms/step - loss: 24437474721792.0000 - val_loss: 17474377482240.0000 Epoch 10/25 54/54 [==============================] - 8s 155ms/step - loss: 28472116248576.0000 - val_loss: 41984181927936.0000 Epoch 11/25 54/54 [==============================] - 8s 156ms/step - loss: 25905709711360.0000 - val_loss: 22885393498112.0000 Epoch 12/25 54/54 [==============================] - 8s 156ms/step - loss: 28368911204352.0000 - val_loss: 21457824382976.0000 Epoch 13/25 54/54 [==============================] - 8s 155ms/step - loss: 24532192591872.0000 - val_loss: 17225914253312.0000 Epoch 14/25 54/54 [==============================] - 8s 156ms/step - loss: 25356499156992.0000 - val_loss: 32382512005120.0000 Epoch 15/25 54/54 [==============================] - 8s 156ms/step - loss: 27916349997056.0000 - val_loss: 28907969445888.0000 Epoch 16/25 54/54 [==============================] - 8s 156ms/step - loss: 22184284127232.0000 - val_loss: 23342392279040.0000 Epoch 17/25 54/54 [==============================] - 8s 156ms/step - loss: 27057742413824.0000 - val_loss: 17813064384512.0000 Epoch 18/25 54/54 [==============================] - 8s 157ms/step - loss: 23184912941056.0000 - val_loss: 15507284557824.0000 Epoch 19/25 54/54 [==============================] - 9s 159ms/step - loss: 23416033771520.0000 - val_loss: 16792128847872.0000 Epoch 20/25 54/54 [==============================] - 8s 156ms/step - loss: 23129709608960.0000 - val_loss: 16191949111296.0000 Epoch 21/25 54/54 [==============================] - 9s 158ms/step - loss: 21248568459264.0000 - val_loss: 17719950835712.0000 Epoch 22/25 54/54 [==============================] - 9s 159ms/step - loss: 20625068392448.0000 - val_loss: 18087147470848.0000 Epoch 23/25 54/54 [==============================] - 8s 156ms/step - loss: 23609357631488.0000 - val_loss: 16003918462976.0000 Epoch 24/25 54/54 [==============================] - 8s 156ms/step - loss: 22228418691072.0000 - val_loss: 18216636121088.0000 Epoch 25/25 54/54 [==============================] - 8s 157ms/step - loss: 19798039724032.0000 - val_loss: 14995342491648.0000
<tensorflow.python.keras.callbacks.History at 0x256d67ebca0>
# Model results for Keras
keras_preds = model.predict(test_features)
print("\nTensorflow Model\n------")
print("RMSE on testing data: {:.4f}".format(sqrt(mean_squared_error(y_test, keras_preds))))
print("r_2 on testing data: {:.4f}".format(r2_score(y_test, keras_preds)))
print("MAE on testing data: {:.4f}".format(mean_absolute_error(y_test, keras_preds)))
Tensorflow Model ------ RMSE on testing data: 3528093.1183 r_2 on testing data: 0.8106 MAE on testing data: 1568674.9879
lineStart = y_test.min()
lineEnd = y_test.max()
plt.figure()
plt.scatter(y_test, keras_preds, color = 'k', alpha=0.5)
plt.plot([lineStart, lineEnd], [lineStart, lineEnd], 'k-', color = 'r')
plt.xlim(lineStart, lineEnd)
plt.ylim(lineStart, lineEnd)
plt.title("Predictions vs Values on test dataset")
plt.xlabel("Values")
plt.ylabel("Predictions")
plt.show()